Cerebral MRI in a prospective cohort study on depression and atherosclerosis: the BiDirect sample, processing pipelines, and analysis tools.
Longitudinal studies
Magnetic resonance imaging
Medical image processing
Population health
Standardization
Journal
European radiology experimental
ISSN: 2509-9280
Titre abrégé: Eur Radiol Exp
Pays: England
ID NLM: 101721752
Informations de publication
Date de publication:
09 Feb 2024
09 Feb 2024
Historique:
received:
26
07
2023
accepted:
23
11
2023
medline:
9
2
2024
pubmed:
9
2
2024
entrez:
9
2
2024
Statut:
epublish
Résumé
The use of cerebral magnetic resonance imaging (MRI) in observational studies has increased exponentially in recent years, making it critical to provide details about the study sample, image processing, and extracted imaging markers to validate and replicate study results. This article reviews the cerebral MRI dataset from the now-completed BiDirect cohort study, as an update and extension of the feasibility report published after the first two examination time points. We report the sample and flow of participants spanning four study sessions and twelve years. In addition, we provide details on the acquisition protocol; the processing pipelines, including standardization and quality control methods; and the analytical tools used and markers available. All data were collected from 2010 to 2021 at a single site in Münster, Germany, starting with a population of 2,257 participants at baseline in 3 different cohorts: a population-based cohort (n = 911 at baseline, 672 with MRI data), patients diagnosed with depression (n = 999, 736 with MRI data), and patients with manifest cardiovascular disease (n = 347, 52 with MRI data). During the study period, a total of 4,315 MRI sessions were performed, and over 535 participants underwent MRI at all 4 time points. Images were converted to Brain Imaging Data Structure (a standard for organizing and describing neuroimaging data) and analyzed using common tools, such as CAT12, FSL, Freesurfer, and BIANCA to extract imaging biomarkers. The BiDirect study comprises a thoroughly phenotyped study population with structural and functional MRI data. The BiDirect Study includes a population-based sample and two patient-based samples whose MRI data can help answer numerous neuropsychiatric and cardiovascular research questions. • The BiDirect study included characterized patient- and population-based cohorts with MRI data. • Data were standardized to Brain Imaging Data Structure and processed with commonly available software. • MRI data and markers are available upon request.
Sections du résumé
BACKGROUND
BACKGROUND
The use of cerebral magnetic resonance imaging (MRI) in observational studies has increased exponentially in recent years, making it critical to provide details about the study sample, image processing, and extracted imaging markers to validate and replicate study results. This article reviews the cerebral MRI dataset from the now-completed BiDirect cohort study, as an update and extension of the feasibility report published after the first two examination time points.
METHODS
METHODS
We report the sample and flow of participants spanning four study sessions and twelve years. In addition, we provide details on the acquisition protocol; the processing pipelines, including standardization and quality control methods; and the analytical tools used and markers available.
RESULTS
RESULTS
All data were collected from 2010 to 2021 at a single site in Münster, Germany, starting with a population of 2,257 participants at baseline in 3 different cohorts: a population-based cohort (n = 911 at baseline, 672 with MRI data), patients diagnosed with depression (n = 999, 736 with MRI data), and patients with manifest cardiovascular disease (n = 347, 52 with MRI data). During the study period, a total of 4,315 MRI sessions were performed, and over 535 participants underwent MRI at all 4 time points.
CONCLUSIONS
CONCLUSIONS
Images were converted to Brain Imaging Data Structure (a standard for organizing and describing neuroimaging data) and analyzed using common tools, such as CAT12, FSL, Freesurfer, and BIANCA to extract imaging biomarkers. The BiDirect study comprises a thoroughly phenotyped study population with structural and functional MRI data.
RELEVANCE STATEMENT
CONCLUSIONS
The BiDirect Study includes a population-based sample and two patient-based samples whose MRI data can help answer numerous neuropsychiatric and cardiovascular research questions.
KEY POINTS
CONCLUSIONS
• The BiDirect study included characterized patient- and population-based cohorts with MRI data. • Data were standardized to Brain Imaging Data Structure and processed with commonly available software. • MRI data and markers are available upon request.
Identifiants
pubmed: 38332362
doi: 10.1186/s41747-023-00415-z
pii: 10.1186/s41747-023-00415-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
16Subventions
Organisme : Bundesministerium für Bildung und Forschung
ID : 01ER0816
Organisme : Bundesministerium für Bildung und Forschung
ID : 01ER1205
Organisme : Bundesministerium für Bildung und Forschung
ID : 01ER1506
Informations de copyright
© 2024. The Author(s).
Références
Teismann H, Wersching H, Nagel M et al (2014) Establishing the bidirectional relationship between depression and subclinical arteriosclerosis – rationale, design, and characteristics of the BiDirect study. BMC Psychiatry 14:174. https://doi.org/10.1186/1471-244X-14-174
doi: 10.1186/1471-244X-14-174
pubmed: 24924233
pmcid: 4065391
Teuber A, Sundermann B, Kugel H et al (2017) MR imaging of the brain in large cohort studies: feasibility report of the population- and patient-based BiDirect study. Eur Radiol 27:231–238. https://doi.org/10.1007/s00330-016-4303-9
doi: 10.1007/s00330-016-4303-9
pubmed: 27059857
Wulms N, Redmann L, Herpertz C, et al (2022) The effect of training sample size on the prediction of white matter hyperintensity volume in a healthy population using BIANCA. Front Aging Neurosci 13 https://doi.org/10.3389/fnagi.2021.720636
Wardlaw JM, Smith EE, Biessels GJ et al (2013) Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 12:822–838. https://doi.org/10.1016/S1474-4422(13)70124-8
doi: 10.1016/S1474-4422(13)70124-8
pubmed: 23867200
pmcid: 3714437
Zaitsev M, Julian M, Herbst M (2015) Motion artefacts in MRI: a complex problem with many partial solutions. J Magn Reson Imaging 42:887–901. https://doi.org/10.1002/jmri.24850
doi: 10.1002/jmri.24850
pubmed: 25630632
pmcid: 4517972
Farahani K, Sinha U, Sinha S et al (1990) Effect of field strength on susceptibility artifacts in magnetic resonance imaging. Comput Med Imaging Graph 14:409–413. https://doi.org/10.1016/0895-6111(90)90040-I
doi: 10.1016/0895-6111(90)90040-I
pubmed: 2272012
Smith AM, Lewis BK, Ruttimann UE et al (1999) Investigation of low frequency drift in fMRI signal. Neuroimage 9:526–533. https://doi.org/10.1006/nimg.1999.0435
doi: 10.1006/nimg.1999.0435
pubmed: 10329292
Esteban O, Birman D, Schaer M et al (2017) MRIQC: advancing the automatic prediction of image quality in MRI from unseen sites. PLoS One 12:e0184661. https://doi.org/10.1371/journal.pone.0184661
Wilkinson MD, Dumontier M, IjJ A et al (2016) The FAIR guiding principles for scientific data management and stewardship. Sci Data 3:160018. https://doi.org/10.1038/sdata.2016.18
doi: 10.1038/sdata.2016.18
pubmed: 26978244
pmcid: 4792175
Nichols TE, Das S, Eickhoff SB et al (2017) Best practices in data analysis and sharing in neuroimaging using MRI. Nat Neurosci 20:299–303. https://doi.org/10.1038/nn.4500
doi: 10.1038/nn.4500
pubmed: 28230846
pmcid: 5685169
Niso G, Botvinik-Nezer R, Appelhoff S et al (2022) Open and reproducible neuroimaging: From study inception to publication. Neuroimage 263:119623. https://doi.org/10.1016/j.neuroimage.2022.119623
doi: 10.1016/j.neuroimage.2022.119623
pubmed: 36100172
Gorgolewski KJ, Auer T, Calhoun VD et al (2016) The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data 3:1–9. https://doi.org/10.1038/sdata.2016.44
doi: 10.1038/sdata.2016.44
Dannlowski U, Ohrmann P, Konrad C et al (2009) Reduced amygdala-prefrontal coupling in major depression: association with MAOA genotype and illness severity. Int J Neuropsychopharmacol 12:11–22. https://doi.org/10.1017/S1461145708008973
doi: 10.1017/S1461145708008973
pubmed: 18544183
Dannlowski U, Ohrmann P, Bauer J et al (2007) Serotonergic genes modulate amygdala activity in major depression. Genes Brain Behav 6:672–676. https://doi.org/10.1111/j.1601-183X.2006.00297.x
doi: 10.1111/j.1601-183X.2006.00297.x
pubmed: 17284168
Wulms N, Eppe S, Dehghan-Nayyeri M et al (2023) The R package for DICOM to brain imaging data structure conversion. Sci Data 10:673. https://doi.org/10.1038/s41597-023-02583-4
doi: 10.1038/s41597-023-02583-4
pubmed: 37794076
pmcid: 10551001
Li X, Morgan PS, Ashburner J et al (2016) The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J Neurosci Methods 264:47–56. https://doi.org/10.1016/j.jneumeth.2016.03.001
doi: 10.1016/j.jneumeth.2016.03.001
pubmed: 26945974
Gaser, Christian CAT12. http://www.neuro.uni-jena.de/cat/ . Accessed 19 Nov 2023
Woolrich MW, Jbabdi S, Patenaude B et al (2009) Bayesian analysis of neuroimaging data in FSL. Neuroimage 45:S173-186. https://doi.org/10.1016/j.neuroimage.2008.10.055
doi: 10.1016/j.neuroimage.2008.10.055
pubmed: 19059349
Smith SM, Jenkinson M, Woolrich MW et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23:208–219. https://doi.org/10.1016/j.neuroimage.2004.07.051
doi: 10.1016/j.neuroimage.2004.07.051
Jenkinson M, Beckmann CF, Behrens TEJ et al (2012) FSL Neuroimage 62:782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015
Griffanti L, Zamboni G, Khan A et al (2016) BIANCA (Brain Intensity AbNormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities. Neuroimage 141:191–205. https://doi.org/10.1016/j.neuroimage.2016.07.018
doi: 10.1016/j.neuroimage.2016.07.018
pubmed: 27402600
Wulms N, Herpertz C, Redmann L, et al (2022) OHBM - White matter hyperintensity (WMH) segmentation in a cohort with few and small WMH. https://doi.org/10.13140/RG.2.2.20102.86089
Baykara E, Gesierich B, Adam R et al (2016) A novel imaging marker for small vessel disease based on skeletonization of white matter tracts and diffusion histograms. Ann Neurol 80:581–592. https://doi.org/10.1002/ana.24758
doi: 10.1002/ana.24758
pubmed: 27518166
Esteban O, Markiewicz CJ, Blair RW et al (2019) fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 16:111–116. https://doi.org/10.1038/s41592-018-0235-4
doi: 10.1038/s41592-018-0235-4
pubmed: 30532080
Sundermann B, Feder S, Wersching H et al (2017) Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample. J Neural Transm 124:589–605. https://doi.org/10.1007/s00702-016-1673-8
doi: 10.1007/s00702-016-1673-8
pubmed: 28040847
Friston KJ, Ashburner J, Kiebel SJ, et al (2007) Statistical parametric mapping: the analysis of functional brain images. Academic Press. ISBN: 978–0–12–372560–8. https://doi.org/10.1016/B978-0-12-372560-8.X5000-1
Microsoft, Weston S (2019) doParallel: Foreach parallel adaptor for the “parallel” package
Microsoft, Weston S (2020) foreach: Provides foreach looping construct
Wickham H, Averick M, Bryan J, et al (2019) Welcome to the tidyverse. JOSS 4:1686. https://doi.org/10.21105/joss.01686
R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria
Naselaris T, Allen E, Kay K (2021) Extensive sampling for complete models of individual brains. Curr Opin Behav Sci 40:45–51. https://doi.org/10.1016/j.cobeha.2020.12.008
doi: 10.1016/j.cobeha.2020.12.008
Elam JS, Glasser MF, Harms MP et al (2021) The human connectome project: a retrospective. Neuroimage 244:118543. https://doi.org/10.1016/j.neuroimage.2021.118543
doi: 10.1016/j.neuroimage.2021.118543
pubmed: 34508893
Berger K, Rietschel M, Rujescu D (2022) The value of ‘mega cohorts’ for psychiatric research. World J Biol Psychiatry 0:1–5. https://doi.org/10.1080/15622975.2021.2011405
Peters A, Peters A, Greiser KH et al (2022) Framework and baseline examination of the German National Cohort (NAKO). Eur J Epidemiol 37:1107–1124. https://doi.org/10.1007/s10654-022-00890-5
doi: 10.1007/s10654-022-00890-5
pubmed: 36260190
pmcid: 9581448
Breteler MMB, Wolf H (2014) P2–135: The Rhineland study: a novel platform for epidemiologic research into Alzheimer disease and related disorders. Alzheimers Dement 10:P520–P520. https://doi.org/10.1016/j.jalz.2014.05.810
doi: 10.1016/j.jalz.2014.05.810
Alfaro-Almagro F, Jenkinson M, Bangerter NK et al (2018) Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166:400–424. https://doi.org/10.1016/j.neuroimage.2017.10.034
doi: 10.1016/j.neuroimage.2017.10.034
pubmed: 29079522
Ikram MA, van der Lugt A, Niessen WJ et al (2015) The Rotterdam scan study: design update 2016 and main findings. Eur J Epidemiol 30:1299–1315. https://doi.org/10.1007/s10654-015-0105-7
doi: 10.1007/s10654-015-0105-7
pubmed: 26650042
pmcid: 4690838
Lamers F, Hoogendoorn AW, Smit JH et al (2012) Sociodemographic and psychiatric determinants of attrition in the Netherlands study of depression and anxiety (NESDA). Compr Psychiatry 53:63–70. https://doi.org/10.1016/j.comppsych.2011.01.011
doi: 10.1016/j.comppsych.2011.01.011
pubmed: 21397218
Sundermann B, Billebaut B, Bauer J et al (2022) Practical aspects of novel MRI techniques in neuroradiology: part 1–3D acquisitions, dixon techniques and artefact reduction. Rofo 194:1100–1108. https://doi.org/10.1055/a-1800-8692
doi: 10.1055/a-1800-8692
pubmed: 35545104