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Atlas of Brain Glucose Metabolism Using Deuterium Metabolic Imaging at 3 T.

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The 2 matches
  1. [1] § Methods › Imaging ↔ Example use of ATLAS.R, lines 1–43 · score 0.76 · basal ganglia, blood glucose, glucose ingestion, brain regions, insula, parietal
  2. [2] § Methods › Statistical Analysis ↔ Example use of ATLAS.R, lines 1–43 · score 0.75 · linear mixed, parsimonious models, comprehensive model, explanatory, regression, variables

Paper

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The authors' code

R · 96 lines · 4.8 KB · CC-BY-4.0 · 2 matches

  1. ### ---- Example use of DMI brain ATLAS ---- ###
  2. # Download this R script and the accompanying rds-files and place them in the same folder.
  3. # Set working directory to location of current file.
  4. library(rstudioapi)
  5. setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
  7. # Load packages
  8. library(data.table)
  9. library(ggplot2)
  10. library(dplyr)
  11. library(purrr)
  12. library(tidyverse)
  13. library(magrittr) # For the %$% composition pipe.
  14. library(Hmisc) # smean.cl.normal, smean.sdl.
  15. library(confintr) # ci_median, ci_sd.
  16. library(lme4) # linear mixed-effects regression (lmer) + includes function bootMer (bootstrapping -> for internal and external validation)
  18. #### Load data ####
  19. # Load linear mixed effects regression models fitted on ATLAS data (30 healthy controls)
  20. lmer_ATLAS_Glx <- readRDS("Comprehensive_Glx.rds")
  21. lmer_ATLAS_Lac <- readRDS("Comprehensive_Lac.rds")
  22. # Comprehensive models include the following variables:
  23. # - Comprehensive_Glx: `Glx/(Glc+HDO)` ~ Segment + Age + Weight + Blood_glc_dif + glc_to_DMI_10x + (1|ID)
  24. # - Comprehensive_Lac: `Lac/(Glc+HDO)` ~ Segment + Sex + Weight + Blood_glc_dif + glc_to_DMI_10x + (1|ID)
  25. # Parsimonious models are as Comprehensive, but without the following variables: Weight, Blood_glc_dif, glc_to_DMI_10x.
  26. # Variable explanation:
  27. # - Segment = Brain region (12 different regions: "Basal_ganglia", "Cerebellum", "Frontal_lobe_L", "Frontal_lobe_R", "Insula", "Occipital_lobe_L", "Occipital_lobe_R", "Parietal_lobe_L", "Parietal_lobe_R", "Temporal_lobe_L", "Temporal_lobe_R", "Thalamus")
  28. # - Age = age of participant (years)
  29. # - Sex = biological sex of participant (factor with 2 levels: Male (1) or Female (2))
  30. # - Weight = weight of participant (kg)
  31. # - Blood_glc_dif = blood glucose increase about 50 minutes from pre-ingestion baseline (mM)
  32. # - glc_to_DMI_10x = time from glucose ingestion of DMI scan (minutes)
  33. # - ID = id of participant (random factor)
  35. # These models can now be used to compare with data on new participants (patients or healthy)
  37. ## Load the data frame with data on new participants. You can also try with the file "dataframeTEST.rds" which contains data on a fictive patient, just for illustration purposes.
  38. dataframeTEST_ATLAS <- readRDS("your_data_frame.rds")
  39. ## make sure that the dataframe contains all relevant informations i.e. the following column names: ID, Group, Segment, Glx/(Glc+HDO), Lac/(Glc+HDO), Sex, Age, Weight, Blood_glc_dif, glc_to_DMI_10x
  40. ## remember to place the dataframe in the same folder as the R script you are looking at right now
  43. #### Predictions (with bootstrapping) ####
  45. ## For all models: create prediction intervals + calculate SD + rename
  46. ## --- Define bootstrap prediction function ---
  47. pred_fun_Glx <- function(fit) predict(fit, newdata = dataframeTEST_ATLAS, re.form = NA)
  48. pred_fun_Lac <- function(fit) predict(fit, newdata = dataframeTEST_ATLAS, re.form = NA)
  51. ## --- Run bootstrapping ---
  52. set.seed(123)
  53. boot_Glx <- bootMer(lmer_ATLAS_Glx, pred_fun_Glx, nsim = 1000, use.u = FALSE, type = "parametric")
  54. boot_Lac <- bootMer(lmer_ATLAS_Lac, pred_fun_Lac, nsim = 1000, use.u = FALSE, type = "parametric")
  56. ## --- Extract prediction intervals and calculate SD ---
  57. Glx_ci <- apply(boot_Glx$t, 2, quantile, probs = c(0.025, 0.5, 0.975))
  58. Lac_ci <- apply(boot_Lac$t, 2, quantile, probs = c(0.025, 0.5, 0.975))
  60. pred_lmer_ATLAS_Glx <- data.frame(
  61. Glx_pred = Glx_ci[2, ],
  62. Glx_lwr = Glx_ci[1, ],
  63. Glx_upr = Glx_ci[3, ]
  64. ) %>%
  65. mutate(Glx_SD = (Glx_upr - Glx_lwr) / (2 * 1.96))
  67. pred_lmer_ATLAS_Lac <- data.frame(
  68. Lac_pred = Lac_ci[2, ],
  69. Lac_lwr = Lac_ci[1, ],
  70. Lac_upr = Lac_ci[3, ]
  71. ) %>%
  72. mutate(Lac_SD = (Lac_upr - Lac_lwr) / (2 * 1.96))
  74. ## Bind predictions to selected original columns
  75. dataframeTEST_ATLAS_pred <- cbind(
  76. dataframeTEST_ATLAS[, c("ID", "Group", "Segment", "Age", "Sex", "Glx/(Glc+HDO)", "Lac/(Glc+HDO)")],
  77. pred_lmer_ATLAS_Glx,
  78. pred_lmer_ATLAS_Lac
  79. )
  81. ## Calculate the observed metabolic deviations from the predicted as:
  82. ### 1) z-score = (observed-predicted)/SD -> how many standard deviations does the observed differ from the predicted?
  83. dataframeTEST_ATLAS_pred <- dataframeTEST_ATLAS_pred %>%
  84. mutate(
  85. Glx_zdif = (`Glx/(Glc+HDO)`-Glx_pred)/Glx_SD,
  86. Lac_zdif = (`Lac/(Glc+HDO)`-Lac_pred)/Lac_SD
  87. )
  88. ### 2) percent = (observed-predicted)/predicted * 100 -> by how many % does the observed differ from the predicted?
  89. dataframeTEST_ATLAS_pred <- dataframeTEST_ATLAS_pred %>%
  90. mutate(
  91. Glx_pdif = (`Glx/(Glc+HDO)`-Glx_pred)/Glx_pred*100,
  92. Lac_pdif = (`Lac/(Glc+HDO)`-Lac_pred)/Lac_pred*100
  93. )
  95. ## Save dataframe as CSV-file (Excel) -> For example to use the prediction deviations (Glx_zdif and Lac_zdif) for colored brain maps
  96. write.csv(dataframeTEST_ATLAS_pred, "dataframe_predictions.csv", row.names = FALSE)

Example use of ATLAS.R, under CC-BY-4.0 · at the source

Overview

Authors: Kamilla K Trosborg1, Nichlas V Christensen1, Malene Aastrup1, Nikolaj Bøgh1, Janne K Mortensen2,3, Hanne Gottrup3, Per Borghammer4, Mattias H Kristensen1, Esben S S Hansen1, Sofie H Christensen5, Jack J Miller1, Rolf F Schulte6, Michael Vaeggemose1,7, Lotte B Bertelsen1, Christoffer Laustsen1
ORCID iDs: Kamilla K Trosborg, Nichlas V Christensen, Nikolaj Bøgh, Janne K Mortensen, Per Borghammer, Esben S S Hansen, Jack J Miller, Michael Vaeggemose, Christoffer Laustsen
  1. The MR Research Centre, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
  2. Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
  3. Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
  4. Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark
  5. Department of Mathematics, Aarhus University, Aarhus, Denmark
  6. GE HealthCare, Munich, Germany
  7. GE HealthCare, Brøndby, Denmark
Institutions: Aarhus University (Denmark); Aarhus University Hospital (Denmark)
Journal: Magnetic resonance in medicine, volume 96, issue 4, pages 1834-1845
Dates: received 7 February 2026; accepted 18 May 2026; published online 30 May 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/mrm.70451 · PMID 42216701 · PMCID PMC13419004 · OpenAlex W7162848824
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population), methods / tools (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity
Keywords: Alzheimer's disease, atlas, brain, deuterium, glucose metabolism, magnetic resonance imaging
MeSH: Brain*, Deuterium*, Glucose*, Magnetic Resonance Imaging*, Aged, Aged, 80 and over, Alzheimer Disease, Female, Humans, Male, Middle Aged (* major topic)
Topic: Advanced MRI Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Novo Nordisk Fonden (0084906); Lundbeck Foundation (R272-2017-4023, R272‐2017‐4023)
Citations: not cited yet (Europe PMC); 66 references in the paper

Abstract

Purpose: Deuterium metabolic imaging (DMI) offers noninvasive magnetic resonance imaging (MRI)–based assessment of metabolism in vivo, making it a relevant paraclinical tool for diseases with neurological metabolic alterations. This study aimed to establish a normative reference atlas of brain glucose metabolism accounting for age and sex.

Methods: DMI were obtained for 30 healthy adults (aged 51–84 years, 15 female) with a 3 T MRI scanner after ingestion of deuterated [6,6'‐2H2]glucose. The images were parcellated to determine the regional distribution of deuterated water, glucose, lactate, and glutamate plus glutamine (Glx). Linear models were applied to investigate the effects of age, sex, and other exploratory adjustments. As a proof‐of‐concept example of atlas application, the normative atlas was compared with patients with Alzheimer's disease and healthy subjects from a previous study.

Results: Regional differences were significant for all metabolites (p < 10−15), with the highest values in the occipital lobes, except for lactate, whose regional distribution pattern was less consistent. While lactate production showed no overall age‐dependency, global Glx production decreased 13% ± 4% per decade. Lactate production tended to be higher in males than females (p = 0.042), but this was not significant after regional adjustment (p = 0.084). Discriminating between health and Alzheimer's disease required additional adjustments for weight, blood glucose, and timing.

Conclusions: While regional and age effects explained a substantial part of the variability in Glx, reliable intersubject comparisons required additional adjustments. The normative atlas presented here provides a reference for future DMI studies of brain metabolism.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

figshare 31241707

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (1 file), ggplot2 (1 file), lme4 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
1 file
At the source: doi.org/10.6084/m9.figshare.31241707

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • 2 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 Statement

Models and example code are openly available in the public repository “DMI normative brain ATLAS” at https://doi.org/10.6084/m9.figshare.31241707. Raw data on DMI is not publicly available to preserve individuals' privacy under the European General Data Protection Regulation.

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 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 6 keywords, 11 MeSH terms, 2 funders, 58 references.

Cite

This paper

Trosborg, K. K., Christensen, N. V., Aastrup, M., Bøgh, N., Mortensen, J. K., Gottrup, H., Borghammer, P., Kristensen, M. H., Hansen, E. S. S., Christensen, S. H., Miller, J. J., Schulte, R. F., Vaeggemose, M., Bertelsen, L. B., & Laustsen, C. (2026). Atlas of Brain Glucose Metabolism Using Deuterium Metabolic Imaging at 3 T. Magnetic resonance in medicine, 96(4), 1834-1845. https://doi.org/10.1002/mrm.70451

BibTeX

@article{trosborg2026atlas,
author = {Trosborg, Kamilla K and Christensen, Nichlas V and Aastrup, Malene and Bøgh, Nikolaj and Mortensen, Janne K and Gottrup, Hanne and Borghammer, Per and Kristensen, Mattias H and Hansen, Esben S S and Christensen, Sofie H and Miller, Jack J and Schulte, Rolf F and Vaeggemose, Michael and Bertelsen, Lotte B and Laustsen, Christoffer},
title = {{Atlas of Brain Glucose Metabolism Using Deuterium Metabolic Imaging at 3 T}},
journal = {Magnetic resonance in medicine},
year = {2026},
month = may,
volume = {96},
number = {4},
pages = {1834--1845},
publisher = {Wiley},
issn = {0740-3194},
doi = {10.1002/mrm.70451},
url = {https://doi.org/10.1002/mrm.70451},
pmid = {42216701},
pmcid = {PMC13419004}
}

RIS

TY - JOUR
AU - Trosborg, Kamilla K
AU - Christensen, Nichlas V
AU - Aastrup, Malene
AU - Bøgh, Nikolaj
AU - Mortensen, Janne K
AU - Gottrup, Hanne
AU - Borghammer, Per
AU - Kristensen, Mattias H
AU - Hansen, Esben S S
AU - Christensen, Sofie H
AU - Miller, Jack J
AU - Schulte, Rolf F
AU - Vaeggemose, Michael
AU - Bertelsen, Lotte B
AU - Laustsen, Christoffer
TI - Atlas of Brain Glucose Metabolism Using Deuterium Metabolic Imaging at 3 T
T2 - Magnetic resonance in medicine
J2 - Magn Reson Med
PY - 2026
DA - 2026/05/30
VL - 96
IS - 4
SP - 1834
EP - 1845
SN - 0740-3194
PB - Wiley
DO - 10.1002/mrm.70451
UR - https://doi.org/10.1002/mrm.70451
LA - en
ER -

CSL-JSON

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