OSCR

Light Exposure as a Modifiable Determinant of Mental Health.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches
  1. [1] § Using Light Exposure to Study Mental Health: Measurement and Intervention Opportunities › Observational Characterization of Light Exposure in Mental Health Conditions ↔ scripts/desc_analysis.qmd, lines 270–413 · score 0.77 · melanopic EDI, Low daytime, low nighttime, High daytime, consecutive, S010
  2. [2] § Using Light Exposure to Study Mental Health: Measurement and Intervention Opportunities › Observational Characterization of Light Exposure in Mental Health Conditions ↔ scripts/Figure_3.qmd, lines 131–168 · score 0.76 · melanopic EDI, Low daytime, low nighttime, High daytime, consecutive, S010
  3. [3] § Monitoring Compliance and Dose in Light Therapy Studies ↔ scripts/Figure_4.qmd, lines 114–149 · score 0.68 · post light, melanopic EDI, pre light, therapy light, Figure 4

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Quarto · 414 lines · 12 KB · CC-BY-4.0 · 1 match

  1. ---
  2. title: "Descriptive analysis"
  3. author: "Johannes Zauner"
  4. format:
  5. html:
  6. self-contained: true
  7. code-tools: true
  8. ---
  10. # Preface
  12. This is a work-in-progress descriptive analysis of the `Sancho-SalasEtAl2025` dataset.
  14. ```{r}
  15. #| label: setup
  16. #| include: false
  17. library(LightLogR)
  18. library(glue)
  19. library(tidyverse)
  20. library(gt)
  21. library(readxl)
  22. library(cowplot)
  23. library(legendry)
  24. library(rnaturalearth)
  25. library(rnaturalearthdata)
  26. library(sf)
  27. library(patchwork)
  28. library(rlang)
  29. library(here)
  31. source("https://raw.githubusercontent.com/MeLiDosProject/Data_Metadata_Conventions/main/scripts/overview_plot.R")
  32. source(
  33. "https://raw.githubusercontent.com/MeLiDosProject/Data_Metadata_Conventions/main/scripts/summary_table.R"
  34. )
  35. ```
  37. # Overview
  39. ## Data import: wearable data
  41. The first step is the import of wearable data from the `head` position (mounted on glasses).
  43. ```{r}
  44. #| label: "general information"
  45. #time zone of Costa Rica
  46. tz <- "America/Costa_Rica"
  47. #coordinates for Costa Rica
  48. coordinates <- c(9.9372, -84.0509)
  49. #regex to extract participant Id and wearing position
  50. # pattern <- "[A-Z]+_S[0-9]{3}_[hcw]"
  51. #regex to extract participant Id
  52. # pattern <- "^([0-9]{3})_"
  53. pattern <- "(UCRS[0-9]{3})_"
  55. country_colors <- c(
  56. Sweden = "#88CCEE", # Sky blue
  57. Spain = "#CC6677", # Coral red
  58. Germany = "#DDCC77", # Mustard yellow
  59. Netherlands= "#117733", # Dark green
  60. Turkey = "#332288", # Indigo
  61. Ghana = "#AA4499", # Purple-pink
  62. Costa_Rica = "#44AA99" # Teal
  63. )
  65. ```
  67. ```{r}
  68. #path to participants
  69. path_part1 <- "data/raw/individual"
  70. #path to actlumus data sans wearing position
  71. path_part2 <- "/continuos/actlumus_"
  72. #wearing position
  73. wearing_position <- "chest"
  74. #getting all subfolders
  75. folders <- dir(here(path_part1))
  76. #creating complete folder names
  77. paths <- glue("{path_part1}/{folders}{path_part2}{wearing_position}")
  78. #collecting file names
  79. files <- list.files(here(paths), full.names = TRUE)
  80. files <- files[str_detect(files, "Report", negate = TRUE)]
  81. ```
  83. ```{r}
  84. #there remain some early data from a pilot collection. these will be removed
  85. data <- import$ActLumus(files, tz, auto.id = pattern, dst_adjustment = TRUE)
  87. #change names to conform to group
  88. data <-
  89. data |>
  90. mutate(Id = fct_relabel(Id, \(x) sprintf("UCR_S%03d", parse_number(x))))
  91. ```
  93. ```{r}
  94. paths_cc <- glue("{path_part1}/{folders}/continuos/currentconditions")
  95. #collecting file names
  96. files_cc <- list.files(here(paths_cc), full.names = TRUE)
  98. current_conditions <- read_csv(files_cc)
  100. current_conditions <-
  101. current_conditions |>
  102. select(Id = record_id, start = startdate_4_v2,
  103. currentlight = currentight_v2, kss = kss_v2) |>
  104. mutate(start = force_tz(start, tz),
  105. start = start - dminutes(30),
  106. end = start + dhours(1),
  107. Id = factor(Id)) |>
  108. drop_na(start) |>
  109. group_by(Id)
  110. ```
  112. ```{r}
  113. #| fig-width: 10
  114. #| fig-height: 5
  115. data <-
  116. data |>
  117. select(Id, Datetime, MEDI) |>
  118. add_states(current_conditions)
  120. data |>
  121. filter(Id %in% c("UCRS001", "UCRS002")) |>
  122. gg_days() |>
  123. gg_state(currentlight, aes_fill = currentlight)
  125. data |>
  126. filter(Id %in% c("UCRS001", "UCRS002", "UCRS003")) |>
  127. gg_heatmap() +
  128. geom_point(data = \(x) x |> drop_na(currentlight),
  129. aes(col = currentlight), size = 4) +
  130. scale_color_manual(values = c(E = "yellow2", I = "skyblue3", L = "green2", O = "skyblue", S = "green3"))
  131. ```
  134. ## Regularizing data
  136. In the first step, we will trim the data by the study time.
  138. ```{r}
  139. #import table with study times
  140. Study_dates <- read_excel("../data/Study_dates_MeLiDos_UCR.xlsx")
  141. #gather the important information
  142. Study_dates <-
  143. Study_dates |>
  144. rename(Id = subjectID_device, start = date_trial_start, end = date_trial_end) |>
  145. select(Id, start, end) |>
  146. mutate(across(c(start, end), \(x) force_tz(x, tz)),
  147. trial = TRUE) |>
  148. drop_na() |>
  149. filter(str_detect(Id, "_c$")) |>
  150. mutate(
  151. Id = str_remove(Id, "_c$"),
  152. Id = factor(Id)) |>
  153. group_by(Id)
  155. #add the trim information to the dataset and filter by it
  156. data <-
  157. data |>
  158. add_states(Study_dates) |>
  159. dplyr::filter(trial) |>
  160. select(-trial)
  162. data |> gg_overview()
  164. # data |> summarize(min = min(Datetime), max = max(Datetime))
  165. ```
  168. ```{r}
  169. data |> has_gaps()
  170. data |> has_irregulars()
  172. data |> gg_gaps(group.by.days = TRUE, show.irregulars = TRUE, full.days = FALSE)
  173. ```
  175. ```{r}
  176. #| fig-height: 15
  177. #| fig-width: 5
  178. data_cleaned <- data |> gap_handler(full.days = TRUE)
  179. data_cleaned |> gap_table(MEDI) |> cols_hide(ends_with("_n"))
  180. ```
  182. ## Exporting hourly values
  184. ```{r}
  185. data_export <-
  186. data_cleaned |>
  187. aggregate_Datetime("1 minute", numeric.handler = \(x) mean(x, na.rm = TRUE)) |>
  188. remove_partial_data(MEDI, threshold.missing = "3 hours", by.date = TRUE)
  190. save(data_export, file = "../data/imported/light/cr_1minute.RData")
  191. ```
  194. ## Visualization
  196. ```{r}
  197. #| warning: false
  198. #| message: false
  199. #| fig-height: 8
  200. #| fig-width: 12
  202. data_cleaned |>
  203. mutate(Id = fct_relabel(Id, \(x) str_remove(x, "UCR_"))) |>
  204. grand_overview(coordinates, "San José", "Costa Rica", "#44AA99", photoperiod_sequence = 0.2, ov_y.text.size = 5)
  206. ggsave("../output/figures/Figure_1.png", width = 17, height = 10, scale = 2, units = "cm")
  207. ggsave("../output/figures/Figure_1.pdf", width = 17, height = 10, scale = 2, units = "cm")
  208. ```
  210. ```{r}
  211. #| message: false
  212. #| warning: false
  214. table_summary <-
  215. light_summary_table(
  216. data_cleaned, coordinates, "San Pedro, San José", "Costa Rica", country_colors["Costa_Rica"],
  217. histograms = TRUE
  218. )
  220. table_summary
  222. gtsave(table_summary, here("output/tables/table_summary.png"), vwidth = 820)
  223. gtsave(table_summary, here("output/tables/table_summary.pdf"))
  224. gtsave(table_summary |> cols_hide(c(plot)), here("output/tables/table_summary.docx"))
  226. ```
  228. ## Adding sleep information
  230. ```{r}
  231. #path to participants
  232. path_part1 <- "data/raw/individual"
  233. #path to sleep diary
  234. path_part3 <- "/continuos/sleepdiary"
  235. #getting all subfolders
  236. folders <- dir(here(path_part1))
  237. #creating complete folder names
  238. paths <- glue("{path_part1}/{folders}{path_part3}")
  239. #collecting file names
  240. files <- list.files(here(paths), full.names = TRUE)
  241. sleep_data <-
  242. import_Statechanges(filename = files[c(3,9,10)],
  243. # sep = ";",
  244. # sep = ",",
  245. # dec = ",",
  246. Id.colname = record_id,
  247. Datetime.format = "mdYHM",
  248. tz = tz,
  249. State.colnames = c("sleep_v2", "offset_v2"),
  250. State.encoding = c("sleep", "wake")
  251. )
  253. #adjusting sleeptime for S009
  254. sleep_data[7,3] <- sleep_data[7,3] + dhours(3)
  256. Brown_data <-
  257. sleep_data |>
  258. sc2interval(full = TRUE, starting.state = "wake") |>
  259. LightLogR::sleep_int2Brown(
  260. Brown.day = "wake",
  261. Brown.evening = "pre-s",
  262. Brown.night = "sleep"
  263. ) |>
  264. mutate(Id = fct_relabel(Id, \(x) sprintf("UCR_S%03d", parse_number(x))))
  267. ```
  270. ## Single out extreme patterns
  272. ```{r}
  273. #| eval: false
  274. #| fig-width: 7
  275. #| fig-height: 7
  276. data_5min <-
  277. data_cleaned |>
  278. select(Id, Datetime, MEDI) |>
  279. aggregate_Datetime("5 mins", numeric.handler = \(x) mean(x, na.rm = TRUE)) |>
  280. remove_partial_data(
  281. MEDI, by.date = TRUE,
  282. ) |>
  283. mutate(.Date = (date(Datetime) - min(date(Datetime)) + 1))
  285. dose_data <-
  286. data_5min |>
  287. add_Date_col(group.by = TRUE) |>
  288. summarize(
  289. dose(
  290. MEDI,
  291. Datetime,
  292. na.rm = TRUE,
  293. as.df = TRUE
  294. ),
  295. .groups = "drop"
  296. )
  298. dose_data |>
  299. ungroup() |>
  300. filter(dose == max(dose) | dose == min(dose))
  302. data_5min |>
  303. gg_day(facetting = FALSE,
  304. group = factor(.Date),
  305. geom = "line",
  306. aes_col = factor(.Date)) +
  307. facet_wrap(~Id, ncol = 2)
  309. data_5min_red <-
  310. data_5min |>
  311. filter(Id %in% c("UCR_S010", "UCR_S003", "UCR_S009"))
  313. data_5min_red <-
  314. data_5min_red |>
  315. interval2state(Brown_data, State.colname = State.Brown) |>
  316. Brown2reference(
  317. Brown.day = "wake",
  318. Brown.evening = "pre-s",
  319. Brown.night = "sleep"
  320. ) |>
  321. mutate(State.Brown = fct_relevel(State.Brown,
  322. "wake", "pre-s", "sleep"))
  324. Plot1 <-
  325. data_5min_red |>
  326. # filter(Id %in% c("UCR_S010") & date(Datetime) == "2025-07-12") |> #High day low night
  327. # filter(Id %in% c("UCR_S003") & date(Datetime) == "2025-06-19") |> #High night
  328. # filter(Id %in% c("UCR_S009") & date(Datetime) == "2025-07-05") |> #low day
  329. filter(date(Datetime) %in% c("2025-07-12", "2025-07-05", "2025-06-19")) |>
  330. # filter(date(Datetime) %in% c("2025-07-01", "2025-07-05")) |>
  331. add_Date_col() |>
  332. add_photoperiod(coordinates) |>
  333. mutate(Id = fct_relevel(Id, "UCR_S010", "UCR_S003", "UCR_S009"),
  334. Id = fct_recode(Id,
  335. "High daytime & low nighttime levels" = "UCR_S010",
  336. "High nighttime levels" = "UCR_S003",
  337. "Low daytime levels" = "UCR_S009"),
  338. # Id = fct_reorder(Id, MEDI, .fun = min, .desc = TRUE),
  339. ) |>
  340. gg_doubleplot(alpha = 0.8,
  341. aes_col = State.Brown,
  342. group = consecutive_id(State.Brown),
  343. # aes_col = factor(Id),
  344. aes_fill = State.Brown,
  345. # aes_fill = factor(Id),
  346. facetting = FALSE,
  347. y.axis.label = "Melanopic EDI (lx)") |>
  348. gg_photoperiod() +
  349. guides(fill = "none", color = "none") +
  350. facet_wrap(~Id, scales = "free_x", ncol = 1) +
  351. coord_cartesian(ylim = c(0,20000)) +
  352. labs(x = NULL) +
  353. theme(
  354. strip.background = element_blank()
  355. ) +
  356. ggplot2::theme_sub_axis(text = element_text(size = 10)) +
  357. theme_sub_strip(text = element_text(face = "bold"))
  359. Plot2 <-
  360. data_5min_red |>
  361. filter(date(Datetime) %in% c("2025-07-12", "2025-07-05", "2025-06-19")) |>
  362. mutate(Id = fct_relevel(Id, "UCR_S010", "UCR_S003", "UCR_S009"),
  363. Id = fct_recode(Id,
  364. " " = "UCR_S010",
  365. " " = "UCR_S003",
  366. " " = "UCR_S009")) |>
  367. group_by(
  368. State.Brown, .add = TRUE
  369. ) |>
  370. summarize(
  371. mean = mean(MEDI, na.rm = TRUE), .groups = "drop"
  372. ) |>
  373. ggplot(aes(x = State.Brown, y = mean)) +
  374. geom_col(aes(fill = State.Brown)) +
  375. facet_wrap(~Id, ncol = 1, scales = "free_x") +
  376. scale_y_continuous(trans = "symlog",
  377. breaks = c(0, 10^(0:5)),
  378. labels =
  379. function(x) format(rep("", 7),
  380. scientific = FALSE, big.mark = " ")) +
  381. coord_cartesian(ylim = c(0,20000)) +
  382. cowplot::theme_cowplot() +
  383. ggsci::scale_fill_jco() +
  384. guides(fill = "none") +
  385. labs(y = "Melanopic EDI (lx)",
  386. x = NULL
  387. ) +
  388. # geom_text(aes(y = 0,
  389. # label = State.Brown |> str_replace("-sleep", "-slp")),
  390. # vjust = -0.1, size = 3) +
  391. # annotate(geom = "text",
  392. # x = 1, y = 20000, label = "wake", color = "black",
  393. # angle = 90, hjust = 1, size = 4)+
  394. # annotate(geom = "text",
  395. # x = 2, y = 20000, label = "pre-sleep", color = "black",
  396. # angle = 90, hjust = 1, size = 4)+
  397. # annotate(geom = "text",
  398. # x = 3, y = 20000, label = "sleep", color = "black",
  399. # angle = 90, hjust = 1, size = 4)+
  400. ggplot2::theme(plot.title.position = "plot",
  401. strip.text.y = ggplot2::element_text(face = "bold",
  402. ), strip.placement = "outside",
  403. panel.grid.major.y = ggplot2::element_line("grey95"),
  404. strip.background = element_blank(),
  405. # strip.text = element_blank(),
  406. plot.margin = ggplot2::margin(10,
  407. 20, 10, 10, "pt")) +
  408. ggplot2::theme_sub_axis(text = element_text(size = 10))
  410. Plot1 + plot_spacer() + Plot2 + plot_layout(widths = c(6,-0.4, 1), axes = "collect")
  412. ggsave("../output/figures/Figure_patterns.png", width = 8*1.5, height = 7*1.5, scale = 2, units = "cm")
  413. ```

desc_analysis.qmd at commit ed90d9a, under CC-BY-4.0 · at the source

Overview

Authors: Manuel Spitschan1,2,3,4, Johannes Zauner1
ORCID iDs: Manuel Spitschan, Johannes Zauner
  1. Department Health and Sports Sciences, TUM School of Medicine and Health, Chronobiology & Health, Technical University of Munich, Munich, Germany
  2. Max Planck Institute for Biological Cybernetics, Max Planck Research Group Translational Sensory & Circadian Neuroscience, Tübingen, Germany
  3. TUM Institute for Advanced Study (TUM-IAS), Technical University of Munich, Garching, Germany
  4. TUMCREATE Ltd., Singapore, Singapore
Institutions: Max Planck Institute for Biological Cybernetics (Germany); TUM CREATE (Singapore); Technical University of Munich (Germany)
Journal: Current psychiatry reports, volume 28, issue 1, article 24
Dates: received 9 February 2026; accepted 9 March 2026; published online 28 April 2026; in print 2026
Type: Review · Language: English
License: CC BY
Identifiers: DOI 10.1007/s11920-026-01671-7 · PMID 42047992 · PMCID PMC13124800 · OpenAlex W7157141178
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), clinical / translational (subfield)
Keywords: Light Exposure, Mental Health, Circadian Rhythms, Exposome, Non-Visual Photoreception, Light-Based Interventions
MeSH: Circadian Rhythm*, Light*, Mental Disorders*, Mental Health*, Phototherapy*, Humans (* major topic)
Topic: Circadian rhythm and melatonin (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex
Funding: Wellcome Trust (226944); Max Planck Institute for Biological Cybernetics
Citations: not cited yet (Europe PMC); 78 references in the paper

Abstract

Purpose of Review: Light is a fundamental environmental signal that shapes human physiology, behaviour, and mental health. Beyond vision, light exposure regulates circadian rhythms, sleep, neuroendocrine function, arousal, and brain circuits implicated in emotional regulation. This review synthesizes recent evidence linking light exposure to mental health and argues that light should be conceptualized as a core, modifiable component of the mental health exposome – the cumulative, dynamic set of environmental influences shaping mental health across the lifespan.

Recent Findings: Recent randomized controlled trials using light therapy, large-scale epidemiological studies, and neurophysiological investigations demonstrate that habitual patterns of daytime and nighttime light exposure are associated with a broad range of mental health outcomes. Higher daytime light exposure is generally associated with better mood and lower depressive symptomatology, whereas greater exposure to light at night is linked to increased risk of depression, anxiety, and sleep disturbance. Advances in wearable light measurement and digital phenotyping now enable precise characterization of individual light environments, supporting observational studies and improving dose verification in light-based interventions. Emerging neurobiological evidence further suggests that light can influence affective brain circuits through pathways that extend beyond sleep and circadian regulation.

Summary: Together, converging evidence positions light exposure as a biologically potent and highly modifiable determinant of mental health operating across multiple temporal scales, from acute alerting effects to longer-term circadian and behavioural adaptation. Conceptual challenges remain, including bidirectionality between light exposure and mental health and limitations in causal inference. Nevertheless, improved measurement technologies and personalized, just-in-time intervention strategies open new opportunities for integrating light exposure into psychiatric research, prevention, and clinical practice.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

tscnlab/SpitschanEtAl_CurrPsychiatryRep_2025

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 1ed6dd975ddafd9fef78e58eda48035b08d6ead9, 6 March 2026
Languages: Quarto (2)
Size: 18 files, 2 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: 2 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: cowplot (2 files), ggplot2 (2 files), patchwork (2 files), tidyverse (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
2 files
At the source: github.com/tscnlab/SpitschanEtAl_CurrPsychiatryRep_2025

Zenodo 17289456

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source:

melidosproject/sancho-salasetal_dataset_2025

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: ed90d9ac91b9fa81c0869dafbe6efb3c8d9faed3, 13 May 2026
Languages: Quarto (20), R (2)
Size: 913 files, 22 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, CITATION.cff, 21 notebooks
Not found: environment file, tests, continuous integration, documentation
Tools: tidyverse (20 files), cowplot (4 files), patchwork (4 files), ggplot2 (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
24 files
At the source: github.com/MeLiDosProject/Sancho-SalasEtAl_Dataset_2025/tree/v1.1.1

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 24 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability

All code and data are available at https://github.com/tscnlab/SpitschanEtAl_CurrPsychiatryRep_2025.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 6 keywords, 6 MeSH terms, 2 funders, 78 references.

Cite

This paper

Spitschan, M., & Zauner, J. (2026). Light Exposure as a Modifiable Determinant of Mental Health. Current psychiatry reports, 28(1), 24. https://doi.org/10.1007/s11920-026-01671-7

BibTeX

@article{spitschan2026light,
author = {Spitschan, Manuel and Zauner, Johannes},
title = {{Light Exposure as a Modifiable Determinant of Mental Health}},
journal = {Current psychiatry reports},
year = {2026},
month = apr,
volume = {28},
number = {1},
pages = {24},
publisher = {Springer Science+Business Media},
issn = {1523-3812},
doi = {10.1007/s11920-026-01671-7},
url = {https://doi.org/10.1007/s11920-026-01671-7},
pmid = {42047992},
pmcid = {PMC13124800}
}

RIS

TY - JOUR
AU - Spitschan, Manuel
AU - Zauner, Johannes
TI - Light Exposure as a Modifiable Determinant of Mental Health
T2 - Current psychiatry reports
J2 - Curr Psychiatry Rep
PY - 2026
DA - 2026/04/28
VL - 28
IS - 1
SP - 24
SN - 1523-3812
PB - Springer Science+Business Media
DO - 10.1007/s11920-026-01671-7
UR - https://doi.org/10.1007/s11920-026-01671-7
LA - en
ER -

CSL-JSON

{
"id": "10.1007/s11920-026-01671-7",
"type": "article-journal",
"title": "Light Exposure as a Modifiable Determinant of Mental Health",
"container-title": "Current psychiatry reports",
"author": [
{
"family": "Spitschan",
"given": "Manuel"
},
{
"family": "Zauner",
"given": "Johannes"
}
],
"container-title-short": "Curr Psychiatry Rep",
"volume": "28",
"issue": "1",
"page": "24",
"DOI": "10.1007/s11920-026-01671-7",
"PMID": "42047992",
"PMCID": "PMC13124800",
"ISSN": "1523-3812",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1007/s11920-026-01671-7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
28
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1162/netn.a.586
Effects of light on brain-state dynamics and energy landscape.
Journal: Network neuroscience (Cambridge, Mass.)
In common: 4 references
[2] doi:10.1002/gps3.70039
Associations between wearable-device-measured daytime and nighttime light exposures and dementia risk: A prospective cohort study.
Journal: General psychiatry
In common: clinical / translational, 3 references
[3] doi:10.1093/neuonc/noag128 [code]
Spatially-resolved single-cell imaging of melanoma brain metastases identifies localized immune patterns predictive of immune checkpoint blockade response.
Journal: Neuro-oncology
In common: cowplot, patchwork, ggplot2, 1 other tool, clinical / translational
[4] doi:10.1002/alz.71567 [code]
Associations of dementia polyexposure scores to Alzheimer's disease endophenotypes in a diverse population.
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association
In common: cowplot, patchwork, ggplot2, 1 other tool, clinical / translational
[5] doi:10.1038/s41514-026-00397-3 [code]
Nasal administration of Protollin enhances monocyte phagocytosis and decreases CD8&lt;sup&gt;+&lt;/sup&gt; T cell cytotoxicity in subjects with early Alzheimer's disease: a Phase 1 clinical trial.
Journal: npj aging
In common: cowplot, patchwork, ggplot2, 1 other tool, clinical / translational
[6] doi:10.1016/j.isci.2026.115657 [code]
Integration of machine learning to develop a disulfidptosis model for predicting glioma prognosis, immunotherapy response, and drug.
Journal: iScience
In common: cowplot, patchwork, ggplot2, 1 other tool, clinical / translational
[7] doi:10.1038/s41588-026-02737-1 [code]
Human and mouse adrenal glands are characterized by species-specific steroidogenic states and tissue turnover.
Journal: Nature genetics
In common: cowplot, patchwork, ggplot2, 1 other tool
[8] doi:10.1093/braincomms/fcag343 [code]
Long-term brain volume trajectories and lifestyle associations in cognitively normal adults: the BRAIN-STRIDE study.
Journal: Brain communications
In common: cowplot, patchwork, ggplot2, 1 other tool
[9] doi:10.1073/pnas.2609814123 [code]
Genetic architectures of brain-related traits are shaped by strong selective constraints.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: cowplot, patchwork, ggplot2, 1 other tool
[10] doi:10.1038/s41467-026-76675-1 [code]
Long-read proteogenomic atlas of human neuronal differentiation reveals isoform diversity informing neurodevelopmental risk mechanisms.
Journal: Nature communications
In common: cowplot, patchwork, ggplot2, 1 other tool

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.