دورية أكاديمية

The MRi-Share database: brain imaging in a cross-sectional cohort of 1870 university students.

التفاصيل البيبلوغرافية
العنوان: The MRi-Share database: brain imaging in a cross-sectional cohort of 1870 university students.
المؤلفون: Tsuchida A; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France., Laurent A; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France., Crivello F; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France., Petit L; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France., Joliot M; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France.; Ginesislab, Fealinx and Université de Bordeaux, Bordeaux, France., Pepe A; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France., Beguedou N; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France., Gueye MF; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France.; Ginesislab, Fealinx and Université de Bordeaux, Bordeaux, France., Verrecchia V; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France.; Ginesislab, Fealinx and Université de Bordeaux, Bordeaux, France., Nozais V; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France.; Ginesislab, Fealinx and Université de Bordeaux, Bordeaux, France., Zago L; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France., Mellet E; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France., Debette S; Université de Bordeaux, INSERM, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France.; Centre Hospitalier Universitaire Pellegrin, Bordeaux, France., Tzourio C; Université de Bordeaux, INSERM, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France.; Centre Hospitalier Universitaire Pellegrin, Bordeaux, France., Mazoyer B; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France. bernard.mazoyer@u-bordeaux.fr.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France. bernard.mazoyer@u-bordeaux.fr.; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France. bernard.mazoyer@u-bordeaux.fr.; Ginesislab, Fealinx and Université de Bordeaux, Bordeaux, France. bernard.mazoyer@u-bordeaux.fr.; Centre Hospitalier Universitaire Pellegrin, Bordeaux, France. bernard.mazoyer@u-bordeaux.fr.
المصدر: Brain structure & function [Brain Struct Funct] 2021 Sep; Vol. 226 (7), pp. 2057-2085. Date of Electronic Publication: 2021 Jul 20.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 101282001 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1863-2661 (Electronic) Linking ISSN: 18632653 NLM ISO Abbreviation: Brain Struct Funct Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Berlin : Springer-Verlag, c2007-
مواضيع طبية MeSH: Brain*/diagnostic imaging , Universities*, Adolescent ; Adult ; Cross-Sectional Studies ; Diffusion Tensor Imaging ; Humans ; Magnetic Resonance Imaging ; Neuroimaging ; Students ; Young Adult
مستخلص: We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1870 young healthy adults, aged 18-35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility-weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early ageing.
(© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
References: 3C Study Group (2003) Vascular factors and risk of dementia: design of the Three-City Study and baseline characteristics of the study population. Neuroepidemiology 22:316–325. https://doi.org/10.1159/000072920. (PMID: 10.1159/000072920)
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. (PMID: 10.1016/j.neuroimage.2017.10.03429079522)
Backhausen LL, Herting MM, Buse J et al (2016) Quality control of structural MRI images applied using FreeSurfer-A hands-on workflow to rate motion artifacts. Front Neurosci 10:558. https://doi.org/10.3389/fnins.2016.00558. (PMID: 10.3389/fnins.2016.00558279995285138230)
Backhouse EV, McHutchison CA, Cvoro V et al (2017) Early life risk factors for cerebrovascular disease: a systematic review and meta-analysis. Neurology 88:976–984. https://doi.org/10.1212/WNL.0000000000003687. (PMID: 10.1212/WNL.000000000000368728188307)
Basser PJ, Mattiello J, LeBihan D (1994) MR diffusion tensor spectroscopy and imaging. Biophys J 66:259–267. https://doi.org/10.1016/S0006-3495(94)80775-1. (PMID: 10.1016/S0006-3495(94)80775-181303441275686)
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. (PMID: 10.1002/ana.2475827518166)
Beaudet G, Tsuchida A, Petit L et al (2020) Age-related changes of peak width skeletonized mean diffusivity (PSMD) across the adult lifespan: a multi-cohort study. Front Psychiatry 11:342. https://doi.org/10.3389/fpsyt.2020.00342. (PMID: 10.3389/fpsyt.2020.00342324258317212692)
Beckmann CF, Smith SM (2004) Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging 23:137–152. https://doi.org/10.1109/TMI.2003.822821. (PMID: 10.1109/TMI.2003.82282114964560)
Caspers S, Moebus S, Lux S et al (2014) Studying variability in human brain aging in a population-based German cohort-rationale and design of 1000BRAINS. Front Aging Neurosci 6:149. https://doi.org/10.3389/fnagi.2014.00149. (PMID: 10.3389/fnagi.2014.00149250715584094912)
Chang YS, Owen JP, Pojman NJ et al (2015) White matter changes of neurite density and fiber orientation dispersion during human brain maturation. PLoS ONE 10:e0123656. https://doi.org/10.1371/journal.pone.0123656. (PMID: 10.1371/journal.pone.0123656261154514482659)
Corley J, Cox SR, Deary IJ (2018) Healthy cognitive ageing in the Lothian Birth Cohort studies: marginal gains not magic bullet. Psychol Med 48:187–207. https://doi.org/10.1017/S0033291717001489. (PMID: 10.1017/S003329171700148928595670)
Coupé P, Catheline G, Lanuza E et al (2017) Towards a unified analysis of brain maturation and aging across the entire lifespan: a MRI analysis. Hum Brain Mapp 38:5501–5518. https://doi.org/10.1002/hbm.23743. (PMID: 10.1002/hbm.23743287372956866824)
Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29:162–173. https://doi.org/10.1006/cbmr.1996.0014. (PMID: 10.1006/cbmr.1996.00148812068)
Cox SR, Ritchie SJ, Tucker-Drob EM et al (2016) Ageing and brain white matter structure in 3513 UK Biobank participants. Nat Commun 7:13629. https://doi.org/10.1038/ncomms13629. (PMID: 10.1038/ncomms13629279766825172385)
Daducci A, Canales-Rodríguez EJ, Zhang H et al (2015) Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data. Neuroimage 105:32–44. https://doi.org/10.1016/j.neuroimage.2014.10.026. (PMID: 10.1016/j.neuroimage.2014.10.02625462697)
Deary IJ, Gow AJ, Taylor MD et al (2007) The Lothian Birth Cohort 1936: a study to examine influences on cognitive ageing from age 11 to age 70 and beyond. BMC Geriatr 7:28. https://doi.org/10.1186/1471-2318-7-28. (PMID: 10.1186/1471-2318-7-28180532582222601)
Debette S, Seshadri S, Beiser A et al (2011) Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline. Neurology 77:461–468. https://doi.org/10.1212/WNL.0b013e318227b227. (PMID: 10.1212/WNL.0b013e318227b227218106963146307)
Desikan RS, Ségonne F, Fischl B et al (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021. (PMID: 10.1016/j.neuroimage.2006.01.02116530430)
Destrieux C, Fischl B, Dale A, Halgren E (2010) Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53:1–15. https://doi.org/10.1016/j.neuroimage.2010.06.010. (PMID: 10.1016/j.neuroimage.2010.06.01020547229)
Diedrichsen J, Balsters JH, Flavell J et al (2009) A probabilistic MR atlas of the human cerebellum. Neuroimage 46:39–46. https://doi.org/10.1016/j.neuroimage.2009.01.045. (PMID: 10.1016/j.neuroimage.2009.01.04519457380)
Ducharme S, Albaugh MD, Nguyen T-V et al (2016) Trajectories of cortical thickness maturation in normal brain development—the importance of quality control procedures. Neuroimage 125:267–279. https://doi.org/10.1016/j.neuroimage.2015.10.010. (PMID: 10.1016/j.neuroimage.2015.10.01026463175)
Dumontheil I (2016) Adolescent brain development. Curr Opin Behav Sci 10:39–44. https://doi.org/10.1016/j.cobeha.2016.04.012. (PMID: 10.1016/j.cobeha.2016.04.012)
Field TS, Doubal FN, Johnson W et al (2016) Early life characteristics and late life burden of cerebral small vessel disease in the Lothian Birth Cohort 1936. Aging (albany NY) 8:2039–2061. https://doi.org/10.18632/aging.101043. (PMID: 10.18632/aging.101043)
Fjell AM, Walhovd KB (2010) Structural brain changes in aging: courses, causes and cognitive consequences. Rev Neurosci 21:187–221. https://doi.org/10.1515/REVNEURO.2010.21.3.187. (PMID: 10.1515/REVNEURO.2010.21.3.18720879692)
Fjell AM, Westlye LT, Amlien I et al (2009) Minute effects of sex on the aging brain: a multisample magnetic resonance imaging study of healthy aging and Alzheimer’s disease. J Neurosci 29:8774–8783. https://doi.org/10.1523/JNEUROSCI.0115-09.2009. (PMID: 10.1523/JNEUROSCI.0115-09.2009195872842782778)
Fjell AM, Walhovd KB, Westlye LT et al (2010) When does brain aging accelerate? Dangers of quadratic fits in cross-sectional studies. Neuroimage 50:1376–1383. https://doi.org/10.1016/j.neuroimage.2010.01.061. (PMID: 10.1016/j.neuroimage.2010.01.06120109562)
Fjell AM, Grydeland H, Krogsrud SK et al (2015) Development and aging of cortical thickness correspond to genetic organization patterns. Proc Natl Acad Sci USA 112:15462–15467. https://doi.org/10.1073/pnas.1508831112. (PMID: 10.1073/pnas.1508831112265756254687601)
Fox J, Friendly M, Monette G (2018) heplots: Visualizing Tests in Multivariate Linear Models. R package version 1.3-5. Version R package version 1.3-5.
Frangou S, Modabbernia A, Williams SCR et al (2021) Cortical thickness across the lifespan: data from 17,075 healthy individuals aged 3–90 years. Hum Brain Mapp. https://doi.org/10.1002/hbm.25364. (PMID: 10.1002/hbm.2536434032348)
Frazier JA, Chiu S, Breeze JL et al (2005) Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder. Am J Psychiatry 162:1256–1265. https://doi.org/10.1176/appi.ajp.162.7.1256. (PMID: 10.1176/appi.ajp.162.7.125615994707)
Garyfallidis E, Brett M, Amirbekian B et al (2014) Dipy, a library for the analysis of diffusion MRI data. Front Neuroinform 8:8. https://doi.org/10.3389/fninf.2014.00008. (PMID: 10.3389/fninf.2014.00008246003853931231)
Genc S, Malpas CB, Holland SK et al (2017) Neurite density index is sensitive to age related differences in the developing brain. Neuroimage 148:373–380. https://doi.org/10.1016/j.neuroimage.2017.01.023. (PMID: 10.1016/j.neuroimage.2017.01.02328087489)
Gennatas ED, Avants BB, Wolf DH et al (2017) Age-related effects and sex differences in gray matter density, volume, mass, and cortical thickness from childhood to young adulthood. J Neurosci 37:5065–5073. https://doi.org/10.1523/JNEUROSCI.3550-16.2017. (PMID: 10.1523/JNEUROSCI.3550-16.2017284321445444192)
Gluckman PD, Hanson MA, Cooper C, Thornburg KL (2008) Effect of in utero and early-life conditions on adult health and disease. N Engl J Med 359:61–73. https://doi.org/10.1056/NEJMra0708473. (PMID: 10.1056/NEJMra0708473185962743923653)
Goldstein JM, Seidman LJ, Makris N et al (2007) Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability. Biol Psychiatry 61:935–945. https://doi.org/10.1016/j.biopsych.2006.06.027. (PMID: 10.1016/j.biopsych.2006.06.02717046727)
Gorgolewski KJ, Burns CD, Madison C et al (2011) Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front Neuroinform 5:13. https://doi.org/10.3389/fninf.2011.00013. (PMID: 10.3389/fninf.2011.00013218978153159964)
Hasan KM, Sankar A, Halphen C et al (2007) Development and organization of the human brain tissue compartments across the lifespan using diffusion tensor imaging. NeuroReport 18:1735–1739. https://doi.org/10.1097/WNR.0b013e3282f0d40c. (PMID: 10.1097/WNR.0b013e3282f0d40c17921878)
Hasan KM, Kamali A, Abid H et al (2010) Quantification of the spatiotemporal microstructural organization of the human brain association, projection and commissural pathways across the lifespan using diffusion tensor tractography. Brain Struct Funct 214:361–373. https://doi.org/10.1007/s00429-009-0238-0. (PMID: 10.1007/s00429-009-0238-0201273572864323)
Herron TJ, Kang X, Woods DL (2015) Sex differences in cortical and subcortical human brain anatomy [version 1; peer review: 1 approved, 1 approved with reservations]. F1000Res. https://doi.org/10.12688/f1000research.6210.1. (PMID: 10.12688/f1000research.6210.1)
Hogstrom LJ, Westlye LT, Walhovd KB, Fjell AM (2013) The structure of the cerebral cortex across adult life: age-related patterns of surface area, thickness, and gyrification. Cereb Cortex 23:2521–2530. https://doi.org/10.1093/cercor/bhs231. (PMID: 10.1093/cercor/bhs23122892423)
Hsu J-L, Leemans A, Bai C-H et al (2008) Gender differences and age-related white matter changes of the human brain: a diffusion tensor imaging study. Neuroimage 39:566–577. https://doi.org/10.1016/j.neuroimage.2007.09.017. (PMID: 10.1016/j.neuroimage.2007.09.01717951075)
Hsu J-L, Van Hecke W, Bai C-H et al (2010) Microstructural white matter changes in normal aging: a diffusion tensor imaging study with higher-order polynomial regression models. Neuroimage 49:32–43. https://doi.org/10.1016/j.neuroimage.2009.08.031. (PMID: 10.1016/j.neuroimage.2009.08.03119699804)
Iglesias JE, Augustinack JC, Nguyen K et al (2015a) A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI. Neuroimage 115:117–137. https://doi.org/10.1016/j.neuroimage.2015.04.042. (PMID: 10.1016/j.neuroimage.2015.04.04225936807)
Iglesias JE, Van Leemput K, Bhatt P et al (2015b) Bayesian segmentation of brainstem structures in MRI. Neuroimage 113:184–195. https://doi.org/10.1016/j.neuroimage.2015.02.065. (PMID: 10.1016/j.neuroimage.2015.02.06525776214)
Ikram MA, Brusselle GGO, Murad SD et al (2017) The Rotterdam Study: 2018 update on objectives, design and main results. Eur J Epidemiol 32:807–850. https://doi.org/10.1007/s10654-017-0321-4. (PMID: 10.1007/s10654-017-0321-4290640095662692)
Jahanshad N, Thompson PM (2017) Multimodal neuroimaging of male and female brain structure in health and disease across the life span. J Neurosci Res 95:371–379. https://doi.org/10.1002/jnr.23919. (PMID: 10.1002/jnr.2391927870421)
Jernigan TL, Brown TT, Hagler DJ et al (2016) The pediatric imaging, neurocognition, and genetics (PING) data repository. Neuroimage 124:1149–1154. https://doi.org/10.1016/j.neuroimage.2015.04.057. (PMID: 10.1016/j.neuroimage.2015.04.05725937488)
Joliot M, Jobard G, Naveau M et al (2015) AICHA: an atlas of intrinsic connectivity of homotopic areas. J Neurosci Methods 254:46–59. https://doi.org/10.1016/j.jneumeth.2015.07.013. (PMID: 10.1016/j.jneumeth.2015.07.01326213217)
Jones DK, Knösche TR, Turner R (2013) White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage 73:239–254. https://doi.org/10.1016/j.neuroimage.2012.06.081. (PMID: 10.1016/j.neuroimage.2012.06.08122846632)
Kaczkurkin AN, Raznahan A, Satterthwaite TD (2019) Sex differences in the developing brain: insights from multimodal neuroimaging. Neuropsychopharmacology 44:71–85. https://doi.org/10.1038/s41386-018-0111-z. (PMID: 10.1038/s41386-018-0111-z29930385)
Kivipelto M, Helkala EL, Hänninen T et al (2001) Midlife vascular risk factors and late-life mild cognitive impairment: a population-based study. Neurology 56:1683–1689. https://doi.org/10.1212/wnl.56.12.1683. (PMID: 10.1212/wnl.56.12.168311425934)
Klein A, Tourville J (2012) 101 labeled brain images and a consistent human cortical labeling protocol. Front Neurosci 6:171. https://doi.org/10.3389/fnins.2012.00171. (PMID: 10.3389/fnins.2012.00171232270013514540)
Kochunov P, Glahn DC, Lancaster J et al (2011) Fractional anisotropy of cerebral white matter and thickness of cortical Gray matter across the lifespan. Neuroimage 58:41–49. https://doi.org/10.1016/j.neuroimage.2011.05.050. (PMID: 10.1016/j.neuroimage.2011.05.05021640837)
Kodiweera C, Alexander AL, Harezlak J et al (2016) Age effects and sex differences in human brain white matter of young to middle-aged adults: a DTI, NODDI, and q-space study. Neuroimage 128:180–192. https://doi.org/10.1016/j.neuroimage.2015.12.033. (PMID: 10.1016/j.neuroimage.2015.12.03326724777)
Kovacs GG, Adle-Biassette H, Milenkovic I et al (2014) Linking pathways in the developing and aging brain with neurodegeneration. Neuroscience 269:152–172. https://doi.org/10.1016/j.neuroscience.2014.03.045. (PMID: 10.1016/j.neuroscience.2014.03.04524699227)
Kurtzer GM, Sochat V, Bauer MW (2017) Singularity: Scientific containers for mobility of compute. PLoS ONE 12:e0177459. https://doi.org/10.1371/journal.pone.0177459. (PMID: 10.1371/journal.pone.0177459284940145426675)
Lebel C, Deoni S (2018) The development of brain white matter microstructure. Neuroimage 182:207–218. https://doi.org/10.1016/j.neuroimage.2017.12.097. (PMID: 10.1016/j.neuroimage.2017.12.09729305910)
Lebel C, Gee M, Camicioli R et al (2012) Diffusion tensor imaging of white matter tract evolution over the lifespan. Neuroimage 60:340–352. https://doi.org/10.1016/j.neuroimage.2011.11.094. (PMID: 10.1016/j.neuroimage.2011.11.09422178809)
Lemaître H, Crivello F, Grassiot B et al (2005) Age- and sex-related effects on the neuroanatomy of healthy elderly. Neuroimage 26:900–911. https://doi.org/10.1016/j.neuroimage.2005.02.042. (PMID: 10.1016/j.neuroimage.2005.02.04215955500)
Loeffler M, Engel C, Ahnert P et al (2015) The LIFE-Adult-Study: objectives and design of a population-based cohort study with 10,000 deeply phenotyped adults in Germany. BMC Public Health 15:691. https://doi.org/10.1186/s12889-015-1983-z. (PMID: 10.1186/s12889-015-1983-z261977794509697)
Madan CR (2018) Age differences in head motion and estimates of cortical morphology. PeerJ 6:e5176. https://doi.org/10.7717/peerj.5176. (PMID: 10.7717/peerj.5176300658586065477)
Mah A, Geeraert B, Lebel C (2017) Detailing neuroanatomical development in late childhood and early adolescence using NODDI. PLoS ONE 12:e0182340. https://doi.org/10.1371/journal.pone.0182340. (PMID: 10.1371/journal.pone.0182340288175775560526)
Makris N, Goldstein JM, Kennedy D et al (2006) Decreased volume of left and total anterior insular lobule in schizophrenia. Schizophr Res 83:155–171. https://doi.org/10.1016/j.schres.2005.11.020. (PMID: 10.1016/j.schres.2005.11.02016448806)
Marcus DS, Fotenos AF, Csernansky JG et al (2010) Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. J Cogn Neurosci 22:2677–2684. https://doi.org/10.1162/jocn.2009.21407. (PMID: 10.1162/jocn.2009.21407199293232895005)
Mazoyer B, Mellet E, Perchey G et al (2016) BIL&GIN: a neuroimaging, cognitive, behavioral, and genetic database for the study of human brain lateralization. Neuroimage 124:1225–1231. https://doi.org/10.1016/j.neuroimage.2015.02.071. (PMID: 10.1016/j.neuroimage.2015.02.07125840118)
McKay DR, Knowles EEM, Winkler AAM et al (2014) Influence of age, sex and genetic factors on the human brain. Brain Imaging Behav 8:143–152. https://doi.org/10.1007/s11682-013-9277-5. (PMID: 10.1007/s11682-013-9277-5242977334011973)
Merluzzi AP, Dean DC, Adluru N et al (2016) Age-dependent differences in brain tissue microstructure assessed with neurite orientation dispersion and density imaging. Neurobiol Aging 43:79–88. https://doi.org/10.1016/j.neurobiolaging.2016.03.026. (PMID: 10.1016/j.neurobiolaging.2016.03.026272558174893194)
Mills KL, Goddings A-L, Herting MM et al (2016) Structural brain development between childhood and adulthood: convergence across four longitudinal samples. Neuroimage 141:273–281. https://doi.org/10.1016/j.neuroimage.2016.07.044. (PMID: 10.1016/j.neuroimage.2016.07.04427453157)
Montagni I, Cariou T, Tzourio C, González-Caballero J-L (2019) “I don’t know”, “I’m not sure”, “I don’t want to answer”: a latent class analysis explaining the informative value of nonresponse options in an online survey on youth health. Int J Soc Res Methodol. https://doi.org/10.1080/13645579.2019.1632026. (PMID: 10.1080/13645579.2019.1632026)
Mori S, Oishi K, Jiang H et al (2008) Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage 40:570–582. https://doi.org/10.1016/j.neuroimage.2007.12.035. (PMID: 10.1016/j.neuroimage.2007.12.03518255316)
Mutlu AK, Schneider M, Debbané M et al (2013) Sex differences in thickness, and folding developments throughout the cortex. Neuroimage 82:200–207. https://doi.org/10.1016/j.neuroimage.2013.05.076. (PMID: 10.1016/j.neuroimage.2013.05.07623721724)
Nordenskjöld R, Malmberg F, Larsson E-M et al (2015) Intracranial volume normalization methods: considerations when investigating gender differences in regional brain volume. Psychiatry Res 231:227–235. https://doi.org/10.1016/j.pscychresns.2014.11.011. (PMID: 10.1016/j.pscychresns.2014.11.01125665840)
Oishi K, Zilles K, Amunts K et al (2008) Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter. Neuroimage 43:447–457. https://doi.org/10.1016/j.neuroimage.2008.07.009. (PMID: 10.1016/j.neuroimage.2008.07.00918692144)
Pausova Z, Paus T, Abrahamowicz M et al (2017) Cohort profile: the saguenay youth study (SYS). Int J Epidemiol 46:e19. https://doi.org/10.1093/ije/dyw023. (PMID: 10.1093/ije/dyw02327018016)
Petersen RC, Aisen PS, Beckett LA et al (2010) Alzheimer’s Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology 74:201–209. https://doi.org/10.1212/WNL.0b013e3181cb3e25. (PMID: 10.1212/WNL.0b013e3181cb3e25200427042809036)
Pfefferbaum A, Sullivan EV (2015) Cross-sectional versus longitudinal estimates of age-related changes in the adult brain: overlaps and discrepancies. Neurobiol Aging 36:2563–2567. https://doi.org/10.1016/j.neurobiolaging.2015.05.005. (PMID: 10.1016/j.neurobiolaging.2015.05.005260597134523414)
Pohl KM, Sullivan EV, Rohlfing T et al (2016) Harmonizing DTI measurements across scanners to examine the development of white matter microstructure in 803 adolescents of the NCANDA study. Neuroimage 130:194–213. https://doi.org/10.1016/j.neuroimage.2016.01.061. (PMID: 10.1016/j.neuroimage.2016.01.06126872408)
Potvin O, Dieumegarde L, Duchesne S, Alzheimer’s Disease Neuroimaging Initiative (2017) Normative morphometric data for cerebral cortical areas over the lifetime of the adult human brain. Neuroimage 156:315–339. https://doi.org/10.1016/j.neuroimage.2017.05.019. (PMID: 10.1016/j.neuroimage.2017.05.01928512057)
R Core Team (2018) R: a language and environment for statistical computing.
Raznahan A, Lee Y, Stidd R et al (2010) Longitudinally mapping the influence of sex and androgen signaling on the dynamics of human cortical maturation in adolescence. Proc Natl Acad Sci USA 107:16988–16993. https://doi.org/10.1073/pnas.1006025107. (PMID: 10.1073/pnas.1006025107208414222947865)
Raznahan A, Lerch JP, Lee N et al (2011a) Patterns of coordinated anatomical change in human cortical development: a longitudinal neuroimaging study of maturational coupling. Neuron 72:873–884. https://doi.org/10.1016/j.neuron.2011.09.028. (PMID: 10.1016/j.neuron.2011.09.028221533814870892)
Raznahan A, Shaw P, Lalonde F et al (2011b) How does your cortex grow? J Neurosci 31:7174–7177. https://doi.org/10.1523/jneurosci.0054-11.2011. (PMID: 10.1523/jneurosci.0054-11.2011215622813157294)
Reuter M, Tisdall MD, Qureshi A et al (2015) Head motion during MRI acquisition reduces Gray matter volume and thickness estimates. Neuroimage 107:107–115. https://doi.org/10.1016/j.neuroimage.2014.12.006. (PMID: 10.1016/j.neuroimage.2014.12.00625498430)
Ritchie SJ, Cox SR, Shen X et al (2018) Sex differences in the adult human brain: evidence from 5216 UK Biobank Participants. Cereb Cortex 28:2959–2975. https://doi.org/10.1093/cercor/bhy109. (PMID: 10.1093/cercor/bhy109297712886041980)
Roalf DR, Quarmley M, Elliott MA et al (2016) The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort. Neuroimage 125:903–919. https://doi.org/10.1016/j.neuroimage.2015.10.068. (PMID: 10.1016/j.neuroimage.2015.10.06826520775)
Sachdev PS, Lammel A, Trollor JN et al (2009) A comprehensive neuropsychiatric study of elderly twins: the Older Australian Twins Study. Twin Res Hum Genet 12:573–582. https://doi.org/10.1375/twin.12.6.573. (PMID: 10.1375/twin.12.6.57319943720)
Sachdev PS, Brodaty H, Reppermund S et al (2010) The Sydney Memory and Ageing Study (MAS): methodology and baseline medical and neuropsychiatric characteristics of an elderly epidemiological non-demented cohort of Australians aged 70–90 years. Int Psychogeriatr 22:1248–1264. https://doi.org/10.1017/S1041610210001067. (PMID: 10.1017/S104161021000106720637138)
Salat DH, Greve DN, Pacheco JL et al (2009) Regional white matter volume differences in nondemented aging and Alzheimer’s disease. Neuroimage 44:1247–1258. https://doi.org/10.1016/j.neuroimage.2008.10.030. (PMID: 10.1016/j.neuroimage.2008.10.03019027860)
Satterthwaite TD, Connolly JJ, Ruparel K et al (2016) The Philadelphia Neurodevelopmental Cohort: a publicly available resource for the study of normal and abnormal brain development in youth. Neuroimage 124:1115–1119. https://doi.org/10.1016/j.neuroimage.2015.03.056. (PMID: 10.1016/j.neuroimage.2015.03.05625840117)
Schumann G, Loth E, Banaschewski T et al (2010) The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol Psychiatry 15:1128–1139. https://doi.org/10.1038/mp.2010.4. (PMID: 10.1038/mp.2010.421102431)
Seiler S, Pirpamer L, Hofer E et al (2014) Magnetization transfer ratio relates to cognitive impairment in normal elderly. Front Aging Neurosci 6:263. https://doi.org/10.3389/fnagi.2014.00263. (PMID: 10.3389/fnagi.2014.00263253094384174770)
Seshadri S, Wolf PA, Beiser A et al (2004) Stroke risk profile, brain volume, and cognitive function: the Framingham Offspring Study. Neurology 63:1591–1599. https://doi.org/10.1212/01.wnl.0000142968.22691.70. (PMID: 10.1212/01.wnl.0000142968.22691.7015534241)
Simmonds DJ, Hallquist MN, Asato M, Luna B (2014) Developmental stages and sex differences of white matter and behavioral development through adolescence: a longitudinal diffusion tensor imaging (DTI) study. Neuroimage 92:356–368. https://doi.org/10.1016/j.neuroimage.2013.12.044. (PMID: 10.1016/j.neuroimage.2013.12.04424384150)
Slater DA, Melie-Garcia L, Preisig M et al (2019) Evolution of white matter tract microstructure across the life span. Hum Brain Mapp 40:2252–2268. https://doi.org/10.1002/hbm.24522. (PMID: 10.1002/hbm.24522306731586865588)
Smith SM, Jenkinson M, Woolrich MW et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(Suppl 1):S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051. (PMID: 10.1016/j.neuroimage.2004.07.05115501092)
Smith SM, Jenkinson M, Johansen-Berg H et al (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31:1487–1505. https://doi.org/10.1016/j.neuroimage.2006.02.024. (PMID: 10.1016/j.neuroimage.2006.02.02416624579)
Sotiras A, Toledo JB, Gur RE et al (2017) Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion. Proc Natl Acad Sci USA 114:3527–3532. https://doi.org/10.1073/pnas.1620928114. (PMID: 10.1073/pnas.1620928114282892245380071)
Soumaré A, Beguedou N, Laurent A et al (2021) Prevalence, severity, and clinical management of brain incidental findings in healthy young adults: MRi-Share cross-sectional study. Front Neurol. https://doi.org/10.3389/fneur.2021.675244. (PMID: 10.3389/fneur.2021.675244340934218173138)
Sowell ER, Peterson BS, Kan E et al (2007) Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. Cereb Cortex 17:1550–1560. https://doi.org/10.1093/cercor/bhl066. (PMID: 10.1093/cercor/bhl06616945978)
Storsve AB, Fjell AM, Tamnes CK et al (2014) Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: regions of accelerating and decelerating change. J Neurosci 34:8488–8498. https://doi.org/10.1523/JNEUROSCI.0391-14.2014. (PMID: 10.1523/JNEUROSCI.0391-14.2014249488046608217)
Suzuki Y, Matsuzawa H, Kwee IL, Nakada T (2003) Absolute eigenvalue diffusion tensor analysis for human brain maturation. NMR Biomed 16:257–260. https://doi.org/10.1002/nbm.848. (PMID: 10.1002/nbm.84814648885)
Tamnes CK, Herting MM, Goddings A-L et al (2017) Development of the cerebral cortex across adolescence: a multisample study of inter-related longitudinal changes in cortical volume, surface area, and thickness. J Neurosci 37:3402–3412. https://doi.org/10.1523/JNEUROSCI.3302-16.2017. (PMID: 10.1523/JNEUROSCI.3302-16.2017282427975373125)
Tukey JW (1977) Exploratory data analysis, 1st edn. Pearson, Reading.
Vijayakumar N, Mills KL, Alexander-Bloch A et al (2018) Structural brain development: a review of methodological approaches and best practices. Dev Cogn Neurosci 33:129–148. https://doi.org/10.1016/j.dcn.2017.11.008. (PMID: 10.1016/j.dcn.2017.11.00829221915)
Vos SB, Jones DK, Viergever MA, Leemans A (2011) Partial volume effect as a hidden covariate in DTI analyses. Neuroimage 55:1566–1576. https://doi.org/10.1016/j.neuroimage.2011.01.048. (PMID: 10.1016/j.neuroimage.2011.01.04821262366)
Wajman JR, Mansur LL, Yassuda MS (2018) Lifestyle patterns as a modifiable risk factor for late-life cognitive decline: a narrative review regarding dementia prevention. Curr Aging Sci 11:90–99. https://doi.org/10.2174/1874609811666181003160225. (PMID: 10.2174/187460981166618100316022530280679)
Walhovd KB, Fjell AM, Reinvang I et al (2005) Effects of age on volumes of cortex, white matter and subcortical structures. Neurobiol Aging 26:1261–1270. https://doi.org/10.1016/j.neurobiolaging.2005.05.020 (discussion 1275). (PMID: 10.1016/j.neurobiolaging.2005.05.02016005549)
Walhovd KB, Westlye LT, Amlien I et al (2011) Consistent neuroanatomical age-related volume differences across multiple samples. Neurobiol Aging 32:916–932. https://doi.org/10.1016/j.neurobiolaging.2009.05.013. (PMID: 10.1016/j.neurobiolaging.2009.05.01319570593)
Wang Y, Adamson C, Yuan W et al (2012) Sex differences in white matter development during adolescence: a DTI study. Brain Res 1478:1–15. https://doi.org/10.1016/j.brainres.2012.08.038. (PMID: 10.1016/j.brainres.2012.08.038229549033592389)
Wardlaw JM, Smith C, Dichgans M (2013) Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging. Lancet Neurol 12:483–497. https://doi.org/10.1016/S1474-4422(13)70060-7. (PMID: 10.1016/S1474-4422(13)70060-723602162)
Westlye LT, Walhovd KB, Dale AM et al (2010) Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry. Cereb Cortex 20:2055–2068. https://doi.org/10.1093/cercor/bhp280. (PMID: 10.1093/cercor/bhp28020032062)
Whalley LJ, Dick FD, McNeill G (2006) A life-course approach to the aetiology of late-onset dementias. Lancet Neurol 5:87–96. https://doi.org/10.1016/S1474-4422(05)70286-6. (PMID: 10.1016/S1474-4422(05)70286-616361026)
White T, El Marroun H, Nijs I et al (2013) Pediatric population-based neuroimaging and the Generation R Study: the intersection of developmental neuroscience and epidemiology. Eur J Epidemiol 28:99–111. https://doi.org/10.1007/s10654-013-9768-0. (PMID: 10.1007/s10654-013-9768-023354984)
Whitmer RA, Gunderson EP, Quesenberry CP et al (2007) Body mass index in midlife and risk of Alzheimer disease and vascular dementia. Curr Alzheimer Res 4:103–109. https://doi.org/10.2174/156720507780362047. (PMID: 10.2174/15672050778036204717430231)
Wierenga LM, Langen M, Oranje B, Durston S (2014) Unique developmental trajectories of cortical thickness and surface area. Neuroimage 87:120–126. https://doi.org/10.1016/j.neuroimage.2013.11.010. (PMID: 10.1016/j.neuroimage.2013.11.01024246495)
Winkler AM, Sabuncu MR, Yeo BTT et al (2012) Measuring and comparing brain cortical surface area and other areal quantities. Neuroimage 61:1428–1443. https://doi.org/10.1016/j.neuroimage.2012.03.026. (PMID: 10.1016/j.neuroimage.2012.03.02622446492)
Yang H, Long X-Y, Yang Y et al (2007) Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI. Neuroimage 36:144–152. https://doi.org/10.1016/j.neuroimage.2007.01.054. (PMID: 10.1016/j.neuroimage.2007.01.05417434757)
Zang Y, Jiang T, Lu Y et al (2004) Regional homogeneity approach to fMRI data analysis. Neuroimage 22:394–400. https://doi.org/10.1016/j.neuroimage.2003.12.030. (PMID: 10.1016/j.neuroimage.2003.12.03015110032)
Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC (2012) NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61:1000–1016. https://doi.org/10.1016/j.neuroimage.2012.03.072. (PMID: 10.1016/j.neuroimage.2012.03.07222484410)
Zou Q-H, Zhu C-Z, Yang Y et al (2008) An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods 172:137–141. https://doi.org/10.1016/j.jneumeth.2008.04.012. (PMID: 10.1016/j.jneumeth.2008.04.012185019693902859)
معلومات مُعتمدة: ANR-10-COHO-05-01 Agence Nationale de la Recherche; ANR-10-LABX-57 Agence Nationale de la Recherche; ANR-16-LCV2-0006 Agence Nationale de la Recherche; ANR-15-HBPR-0001-03 Agence Nationale de la Recherche; ANR-18- RHUS-002 Agence Nationale de la Recherche; 4370420 Conseil Régional Aquitaine; 640643 H2020 European Research Council; DIC202161236446 Fondation pour la Recherche Médicale (FR)
فهرسة مساهمة: Keywords: Brain; Cohort; Cross-sectional; MRI; Post-adolescence; Student
تواريخ الأحداث: Date Created: 20210720 Date Completed: 20220131 Latest Revision: 20220131
رمز التحديث: 20240628
DOI: 10.1007/s00429-021-02334-4
PMID: 34283296
قاعدة البيانات: MEDLINE
الوصف
تدمد:1863-2661
DOI:10.1007/s00429-021-02334-4