[en] The regional integrity of brain subcortical structures has been implicated in sleep-wake regulation, however, their associations with sleep parameters remain largely unexplored. Here, we assessed association between quantitative Magnetic Resonance Imaging (qMRI)-derived marker of the myelin content of the brainstem and the variability in the sleep electrophysiology in a large sample of 18-to-31 years healthy young men (N = 321; ~ 22 years). Separate Generalized Additive Model for Location, Scale and Shape (GAMLSS) revealed that sleep onset latency and slow wave energy were significantly associated with MTsat estimates in the brainstem (pcorrected ≤ 0.03), with overall higher MTsat value associated with values reflecting better sleep quality. The association changed with age, however (MTsat-by-age interaction-pcorrected ≤ 0.03), with higher MTsat value linked to better values in the two sleep metrics in the younger individuals of our sample aged ~ 18 to 20 years. Similar associations were detected across different parts of the brainstem (pcorrected ≤ 0.03), suggesting that the overall maturation and integrity of the brainstem was associated with both sleep metrics. Our results suggest that myelination of the brainstem nuclei essential to regulation of sleep is associated with inter-individual differences in sleep characteristics during early adulthood. They may have implications for sleep disorders or neurological diseases related to myelin.
Disciplines :
Human health sciences: Multidisciplinary, general & others
Author, co-author :
Talwar, Puneet ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Deantoni, Michele ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Van Egroo, Maxime; GIGA-Institute, CRC-In Vivo Imaging Unit, Bâtiment B30, Université de Liège, 4000, Liège, Belgium ; Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, The Netherlands
Muto, Vincenzo ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology ; Walloon Excellence in Life Sciences and Biotechnology (WELBIO), Wallonia, Belgium
Chylinski, Daphné ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Koshmanova, Ekaterina ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Jaspar, Mathieu ; Université de Liège - ULiège > Unités de recherche interfacultaires > GIGA-CRC In vivo Imaging (Centre de Recherche du Cyclotron) ; Walloon Excellence in Life Sciences and Biotechnology (WELBIO), Wallonia, Belgium
Meyer, Christelle ; Université de Liège - ULiège > Unités de recherche interfacultaires > GIGA-CRC In vivo Imaging (Centre de Recherche du Cyclotron) ; Walloon Excellence in Life Sciences and Biotechnology (WELBIO), Wallonia, Belgium
Degueldre, Christian ; Université de Liège - ULiège > Unités de recherche interfacultaires > GIGA-CRC In vivo Imaging (Centre de Recherche du Cyclotron)
Berthomier, Christian; Physip, Paris, France
Luxen, André ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging
Salmon, Eric ; Centre Hospitalier Universitaire de Liège - CHU > > Service de neurologie ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging
Collette, Fabienne ; Université de Liège - ULiège > Département de Psychologie ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging
Dijk, D-J; Sleep Research Centre, University of Surrey, Guildford, UK ; UK Dementia Research Institute, University of Surrey, Guildford, UK
Schmidt, Christina ; Université de Liège - ULiège > Département de Psychologie > Neuropsychologie de l'adulte ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Phillips, Christophe ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Neuroimaging, data acquisition and processing ; ULiège - University of Liège [BE] > GIGA-In Silico Medicine
Maquet, Pierre ; Centre Hospitalier Universitaire de Liège - CHU > > Service de neurologie ; Walloon Excellence in Life Sciences and Biotechnology (WELBIO), Wallonia, Belgium
Sherif, Siya ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Vandewalle, Gilles ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE] UK DRI - UK Dementia Research Institute [GB] FWB - Fédération Wallonie-Bruxelles [BE] WELBIO - Walloon Excellence in Life Sciences and Biotechnology [BE] FRA - Fondation pour la Recherche sur la Maladie d'Alzheimer [FR] ULiège - Université de Liège [BE] Fondation Pierre et Simone Clerdent [BE] ERDF - European Regional Development Fund [BE] Fonds Léon Fredericq [BE]
Funding text :
PT, MD, MVE, EK, FC, CS, CP and GV are/were supported by the Fonds de la Recherche Scientifique—FNRS-Belgium. The study was supported by the Wallonia-Brussels Federation (Actions de Recherche Concertées—ARC—09/14-03), WELBIO/Walloon Excellence in Life Sciences and Biotechnology Grant (WELBIOCR-2010-06E), FNRS-Belgium (FRS-FNRS, F.4513.17 and T.0242.19 and 3.4516.11), Fondation Recherche Alzheimer (SAO-FRA 2019/0025), University of Liège (ULiège), Fondation Simone et Pierre Clerdent, European Regional Development Fund (Radiomed project), Fonds Léon Fredericq. D.J.D. is supported by the UK Dementia Research Institute (DRI). We acknowledge Christian Lambert for providing the scripts to perform the brainstem segmentation.
Zeitzer, J. M. Control of sleep and wakefulness in health and disease. Prog. Mol. Biol. Transl. Sci. 119, 137–154. 10.1016/B978-0-12-396971-2.00006-3 (2013). DOI: 10.1016/B978-0-12-396971-2.00006-3
Carrier, J. et al. Sleep slow wave changes during the middle years of life. Eur. J. Neurosci. 33, 758–766. 10.1111/j.1460-9568.2010.07543.x (2011). DOI: 10.1111/j.1460-9568.2010.07543.x
Tucker, A. M., Dinges, D. F. & Van Dongen, H. P. Trait interindividual differences in the sleep physiology of healthy young adults. J. Sleep Res. 16, 170–180. 10.1111/j.1365-2869.2007.00594.x (2007). DOI: 10.1111/j.1365-2869.2007.00594.x
Scammell, T. E., Arrigoni, E. & Lipton, J. O. Neural circuitry of wakefulness and sleep. Neuron 93, 747–765. 10.1016/j.neuron.2017.01.014 (2017). DOI: 10.1016/j.neuron.2017.01.014
Anaclet, C. & Fuller, P. M. Brainstem regulation of slow-wave-sleep. Curr. Opin. Neurobiol. 44, 139–143. 10.1016/j.conb.2017.04.004 (2017). DOI: 10.1016/j.conb.2017.04.004
Kayama, Y. & Koyama, Y. Control of sleep and wakefulness by brainstem monoaminergic and cholinergic neurons. Acta Neurochir. Suppl. 87, 3–6. 10.1007/978-3-7091-6081-7_1 (2003). DOI: 10.1007/978-3-7091-6081-7_1
Benarroch, E. E. Brainstem integration of arousal, sleep, cardiovascular, and respiratory control. Neurology 91, 958–966. 10.1212/wnl.0000000000006537 (2018). DOI: 10.1212/wnl.0000000000006537
Osorio-Forero, A. et al. Noradrenergic circuit control of non-REM sleep substates. Curr. Biol. 31, 5009-5023 e5007. 10.1016/j.cub.2021.09.041 (2021). DOI: 10.1016/j.cub.2021.09.041
Van Egroo, M., van Hooren, R. W. E. & Jacobs, H. I. L. Associations between locus coeruleus integrity and nocturnal awakenings in the context of Alzheimer’s disease plasma biomarkers: A 7T MRI study. Alzheimer’s Res. Ther. 13, 159. 10.1186/s13195-021-00902-8 (2021). DOI: 10.1186/s13195-021-00902-8
Van Egroo, M., Koshmanova, E., Vandewalle, G. & Jacobs, H. I. L. Importance of the locus coeruleus-norepinephrine system in sleep–wake regulation: Implications for aging and Alzheimer’s disease. Sleep Med. Rev. 62, 101592. 10.1016/j.smrv.2022.101592 (2022). DOI: 10.1016/j.smrv.2022.101592
Dube, J. et al. Cortical thinning explains changes in sleep slow waves during adulthood. J. Neurosci. 35, 7795–7807. 10.1523/JNEUROSCI.3956-14.2015 (2015). DOI: 10.1523/JNEUROSCI.3956-14.2015
Van Egroo, M. et al. Sleep–wake regulation and the hallmarks of the pathogenesis of Alzheimer’s disease. Sleep 42, zsz017. 10.1093/sleep/zsz017 (2019). DOI: 10.1093/sleep/zsz017
Fitzroy, A. B., Kainec, K. A. & Spencer, R. M. C. Ageing-related changes in nap neuroscillatory activity are mediated and moderated by grey matter volume. Eur. J. Neurosci. 54, 7332–7354. 10.1111/ejn.15468 (2021). DOI: 10.1111/ejn.15468
Jamieson, D., Broadhouse, K. M., Lagopoulos, J. & Hermens, D. F. Investigating the links between adolescent sleep deprivation, fronto-limbic connectivity and the Onset of Mental Disorders: A review of the literature. Sleep Med. 66, 61–67. 10.1016/j.sleep.2019.08.013 (2020). DOI: 10.1016/j.sleep.2019.08.013
Arain, M. et al. Maturation of the adolescent brain. Neuropsychiatr. Dis. Treat. 9, 449–461. 10.2147/ndt.s39776 (2013). DOI: 10.2147/ndt.s39776
Bouhrara, M. et al. Maturation and degeneration of the human brainstem across the adult lifespan. Aging 13, 14862–14891. 10.18632/aging.203183 (2021). DOI: 10.18632/aging.203183
Gillberg, M. & Akerstedt, T. The dynamics of the first sleep cycle. Sleep 14, 147–154 (1991).
Basiri, N., Khayyer, Z., Hadianfard, H. & Ghaderi, A. Comparison of the effectiveness of cognitive behavioral therapy and neurofeedback: Reducing insomnia symptoms. Glob. J. Health Sci. 9, 35 (2017). DOI: 10.5539/gjhs.v9n7p35
Riemann, D. et al. REM sleep instability—A new pathway for insomnia?. Pharmacopsychiatry 45, 167–176. 10.1055/s-0031-1299721 (2012). DOI: 10.1055/s-0031-1299721
Berthomier, C. et al. Automatic analysis of single-channel sleep EEG: Validation in healthy individuals. Sleep 30, 1587–1595. 10.1093/sleep/30.11.1587 (2007). DOI: 10.1093/sleep/30.11.1587
Schmierer, K., Scaravilli, F., Altmann, D. R., Barker, G. J. & Miller, D. H. Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann. Neurol. 56, 407–415. 10.1002/ana.20202 (2004). DOI: 10.1002/ana.20202
Li, J., Vitiello, M. V. & Gooneratne, N. S. Sleep in normal aging. Sleep Med. Clin. 13, 1–11. 10.1016/j.jsmc.2017.09.001 (2018). DOI: 10.1016/j.jsmc.2017.09.001
Sprecher, K. E. et al. High resolution topography of age-related changes in non-rapid eye movement sleep electroencephalography. PLoS One 11, e0149770. 10.1371/journal.pone.0149770 (2016). DOI: 10.1371/journal.pone.0149770
Baker, F. C. et al. Age-related differences in sleep architecture and electroencephalogram in adolescents in the national consortium on alcohol and neurodevelopment in adolescence sample. Sleep 39, 1429–1439. 10.5665/sleep.5978 (2016). DOI: 10.5665/sleep.5978
Marmolejo-Ramos, F. et al. Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 13, e1479 (2023). DOI: 10.1002/widm.1479
de Vivo, L. & Bellesi, M. The role of sleep and wakefulness in myelin plasticity. Glia 67, 2142–2152. 10.1002/glia.23667 (2019). DOI: 10.1002/glia.23667
McDougall, S. et al. Myelination of axons corresponds with faster transmission speed in the prefrontal cortex of developing male rats. eNeuro 10.1523/ENEURO.0203-18.2018 (2018). DOI: 10.1523/ENEURO.0203-18.2018
Weiskopf, N. et al. Quantitative multi-parameter mapping of R1, PD(*), MT, and R2(*) at 3T: A multi-center validation. Front. Neurosci. 7, 95. 10.3389/fnins.2013.00095 (2013). DOI: 10.3389/fnins.2013.00095
Laule, C. et al. Magnetic resonance imaging of myelin. Neurotherapeutics 4, 460–484. https://doi.org/10.1016/j.nurt.2007.05.004 (2007).
Callaghan, M. F. et al. Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging. Neurobiol Aging 35, 1862–1872. https://doi.org/10.1016/j.neurobiolaging.2014.02.008 (2014).
Nunez, P. L., Srinivasan, R. & Fields, R. D. EEG functional connectivity, axon delays and white matter disease. Clin. Neurophysiol. 126, 110–120. 10.1016/j.clinph.2014.04.003 (2015). DOI: 10.1016/j.clinph.2014.04.003
Zatorre, R. J., Fields, R. D. & Johansen-Berg, H. Plasticity in gray and white: Neuroimaging changes in brain structure during learning. Nat. Neurosci. 15, 528–536. 10.1038/nn.3045 (2012). DOI: 10.1038/nn.3045
Hagenauer, M. H. & Lee, T. M. Adolescent sleep patterns in humans and laboratory animals. Horm. Behav. 64, 270–279. 10.1016/j.yhbeh.2013.01.013 (2013). DOI: 10.1016/j.yhbeh.2013.01.013
Feinberg, I., de Bie, E., Davis, N. M. & Campbell, I. G. Topographic differences in the adolescent maturation of the slow wave EEG during NREM sleep. Sleep 34, 325–333. 10.1093/sleep/34.3.325 (2011). DOI: 10.1093/sleep/34.3.325
Campbell, I. G. & Feinberg, I. Longitudinal trajectories of non-rapid eye movement delta and theta EEG as indicators of adolescent brain maturation. Proc. Natl. Acad. Sci. USA 106, 5177–5180. 10.1073/pnas.0812947106 (2009). DOI: 10.1073/pnas.0812947106
Osorio-Forero, A., Cherrad, N., Banterle, L., Fernandez, L. M. J. & Luthi, A. When the locus coeruleus speaks up in sleep: Recent insights, emerging perspectives. Int. J. Mol. Sci. 10.3390/ijms23095028 (2022). DOI: 10.3390/ijms23095028
Kato, T. et al. Oscillatory population-level activity of dorsal raphe serotonergic neurons is inscribed in sleep structure. J. Neurosci. 42, 7244–7255. 10.1523/JNEUROSCI.2288-21.2022 (2022). DOI: 10.1523/JNEUROSCI.2288-21.2022
Xu, M. et al. Basal forebrain circuit for sleep–wake control. Nat. Neurosci. 18, 1641–1647. 10.1038/nn.4143 (2015). DOI: 10.1038/nn.4143
Ni, K. M. et al. Selectively driving cholinergic fibers optically in the thalamic reticular nucleus promotes sleep. eLife 10.7554/eLife.10382 (2016). DOI: 10.7554/eLife.10382
Chieffi, S. et al. Orexin system: The key for a healthy life. Front. Physiol. 8, 357. 10.3389/fphys.2017.00357 (2017). DOI: 10.3389/fphys.2017.00357
Young, K. M. et al. Oligodendrocyte dynamics in the healthy adult CNS: Evidence for myelin remodeling. Neuron 77, 873–885. 10.1016/j.neuron.2013.01.006 (2013). DOI: 10.1016/j.neuron.2013.01.006
Yeung, M. S. et al. Dynamics of oligodendrocyte generation and myelination in the human brain. Cell 159, 766–774. 10.1016/j.cell.2014.10.011 (2014). DOI: 10.1016/j.cell.2014.10.011
Bellesi, M. et al. Effects of sleep and wake on oligodendrocytes and their precursors. J. Neurosci. 33, 14288–14300. 10.1523/jneurosci.5102-12.2013 (2013). DOI: 10.1523/jneurosci.5102-12.2013
Puentes-Mestril, C., Roach, J., Niethard, N., Zochowski, M. & Aton, S. J. How rhythms of the sleeping brain tune memory and synaptic plasticity. Sleep 10.1093/sleep/zsz095 (2019). DOI: 10.1093/sleep/zsz095
Cirelli, C. The genetic and molecular regulation of sleep: From fruit flies to humans. Nat. Rev. Neurosci. 10, 549–560. 10.1038/nrn2683 (2009). DOI: 10.1038/nrn2683
Kurth, S. et al. Increased sleep depth in developing neural networks: New insights from sleep restriction in children. Front. Hum. Neurosci. 10, 456. 10.3389/fnhum.2016.00456 (2016). DOI: 10.3389/fnhum.2016.00456
Deantoni, M. et al. Association between sleep slow-wave activity and in-vivo estimates of myelin in healthy young men. Neuroimage 272, 120045. 10.1016/j.neuroimage.2023.120045 (2023). DOI: 10.1016/j.neuroimage.2023.120045
Saccenti, L. et al. Myelin measurement using quantitative magnetic resonance imaging: A correlation study comparing various imaging techniques in patients with multiple sclerosis. Cells 10.3390/cells9020393 (2020). DOI: 10.3390/cells9020393
Edwards, L. J. et al. Quantitative MRI maps of human neocortex explored using cell type-specific gene expression analysis. Cereb. Cortex 33, 5704–5716. 10.1093/cercor/bhac453 (2023). DOI: 10.1093/cercor/bhac453
Stuber, C. et al. Myelin and iron concentration in the human brain: A quantitative study of MRI contrast. Neuroimage 93(Pt 1), 95–106. 10.1016/j.neuroimage.2014.02.026 (2014). DOI: 10.1016/j.neuroimage.2014.02.026
Bartlett, D. M. et al. Investigating the relationships between hypothalamic volume and measures of circadian rhythm and habitual sleep in premanifest Huntington’s disease. Neurobiol. Sleep Circadian Rhythms 6, 1–8. 10.1016/j.nbscr.2018.07.001 (2019). DOI: 10.1016/j.nbscr.2018.07.001
Kreckova, M. et al. Anterior hippocampus volume loss in narcolepsy with cataplexy. J. Sleep Res. 28, e12785. 10.1111/jsr.12785 (2019). DOI: 10.1111/jsr.12785
Bazin, P. L., Alkemade, A., Mulder, M. J., Henry, A. G. & Forstmann, B. U. Multi-contrast anatomical subcortical structures parcellation. eLife 10.7554/eLife.59430 (2020). DOI: 10.7554/eLife.59430
Schutz, L., Sixel-Doring, F. Hermann, W. Management of Sleep Disturbances in Parkinson's Disease. Journal of Parkinson's disease 12, 2029–2058. https://doi.org/10.3233/JPD-212749 (2022).
Koltuniuk, A., Kazimierska-Zajac, M., Poglodek, D. & Chojdak-Lukasiewicz, J. Sleep disturbances, degree of disability and the quality of life in multiple sclerosis patients. Int. J. Environ. Res. Public Health 10.3390/ijerph19063271 (2022). DOI: 10.3390/ijerph19063271
Foschi, M. et al. Sleep-related disorders and their relationship with MRI findings in multiple sclerosis. Sleep medicine 56, 90–97. https://doi.org/10.1016/j.sleep.2019.01.010 (2019).
Muto, V. et al. Alzheimer’s disease genetic risk and sleep phenotypes in healthy young men: Association with more slow waves and daytime sleepiness. Sleep 10.1093/sleep/zsaa137 (2021). DOI: 10.1093/sleep/zsaa137
Beck, A. T., Epstein, N., Brown, G. & Steer, R. Beck anxiety inventory. J. Consult. Clin. Psychol. 56, 893–897 (1993). DOI: 10.1037/0022-006X.56.6.893
Beck, A. T., Steer, R. A. & Carbin, M. G. Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation. Clin. Psychol. Rev. 8, 77–100 (1988). DOI: 10.1016/0272-7358(88)90050-5
Buysse, D. J., Reynolds, C. F. 3rd., Monk, T. H., Berman, S. R. & Kupfer, D. J. The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Res. 28, 193–213. 10.1016/0165-1781(89)90047-4 (1989). DOI: 10.1016/0165-1781(89)90047-4
Johns, M. W. A new method for measuring daytime sleepiness: The Epworth sleepiness scale. Sleep 14, 540–545. 10.1093/sleep/14.6.540 (1991). DOI: 10.1093/sleep/14.6.540
Berthomier, C. et al. Exploring scoring methods for research studies: Accuracy and variability of visual and automated sleep scoring. J. Sleep Res. 29, e12994. 10.1111/jsr.12994 (2020). DOI: 10.1111/jsr.12994
t Wallant, D. C. et al. Automatic artifacts and arousals detection in whole-night sleep EEG recordings. J. Neurosci. Methods 258, 124–133. 10.1016/j.jneumeth.2015.11.005 (2016). DOI: 10.1016/j.jneumeth.2015.11.005
Dijk, D. J. & Landolt, H. P. Sleep physiology, circadian rhythms, waking performance and the development of sleep–wake therapeutics. Handb. Exp. Pharmacol. 253, 441–481. 10.1007/164_2019_243 (2019). DOI: 10.1007/164_2019_243
Dijk, D. J. & Czeisler, C. A. Contribution of the circadian pacemaker and the sleep homeostat to sleep propensity, sleep structure, electroencephalographic slow waves, and sleep spindle activity in humans. J. Neurosci. 15, 3526–3538 (1995). DOI: 10.1523/JNEUROSCI.15-05-03526.1995
Cajochen, C., Khalsa, S. B., Wyatt, J. K., Czeisler, C. A. & Dijk, D. J. EEG and ocular correlates of circadian melatonin phase and human performance decrements during sleep loss. Am. J. Physiol. 277, R640–R649. 10.1152/ajpregu.1999.277.3.r640 (1999). DOI: 10.1152/ajpregu.1999.277.3.r640
Tabelow, K. et al. hMRI—A toolbox for quantitative MRI in neuroscience and clinical research. Neuroimage 194, 191–210. 10.1016/j.neuroimage.2019.01.029 (2019). DOI: 10.1016/j.neuroimage.2019.01.029
Weiskopf, N., Mohammadi, S., Lutti, A. & Callaghan, M. F. Advances in MRI-based computational neuroanatomy: From morphometry to in-vivo histology. Curr. Opin. Neurol. 28, 313–322. 10.1097/WCO.0000000000000222 (2015). DOI: 10.1097/WCO.0000000000000222
Lutti, A. et al. Robust and fast whole brain mapping of the RF transmit field B1 at 7T. PLoS One 7, e32379. 10.1371/journal.pone.0032379 (2012). DOI: 10.1371/journal.pone.0032379
Helms, G., Weiskopf, N. Lutti, A. Correction of FLASH-based MT saturation in human brain for residual bias of B1-inhomogeneity at 3T. arXiv preprint arXiv:2104.14878 (2021).
Yarnykh, V. L. Actual flip-angle imaging in the pulsed steady state: A method for rapid three-dimensional mapping of the transmitted radiofrequency field. Magn. Reason. Med. 57, 192–200. 10.1002/mrm.21120 (2007). DOI: 10.1002/mrm.21120
Ashburner, J. & Friston, K. J. Unified segmentation. NeuroImage 26, 839–851. 10.1016/j.neuroimage.2005.02.018 (2005). DOI: 10.1016/j.neuroimage.2005.02.018
Ashburner, J. & Friston, K. J. Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation. NeuroImage 55, 954–967. 10.1016/j.neuroimage.2010.12.049 (2011). DOI: 10.1016/j.neuroimage.2010.12.049
Lambert, C., Lutti, A., Helms, G., Frackowiak, R. & Ashburner, J. Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians. NeuroImage Clin. 2, 684–694. 10.1016/j.nicl.2013.04.017 (2013). DOI: 10.1016/j.nicl.2013.04.017
Ridgway, G. R. et al. Issues with threshold masking in voxel-based morphometry of atrophied brains. NeuroImage 44, 99–111. 10.1016/j.neuroimage.2008.08.045 (2009). DOI: 10.1016/j.neuroimage.2008.08.045
Ashburner, J. A fast diffeomorphic image registration algorithm. NeuroImage 38, 95–113. 10.1016/j.neuroimage.2007.07.007 (2007). DOI: 10.1016/j.neuroimage.2007.07.007
Maldjian, J. A., Laurienti, P. J., Kraft, R. A. & Burdette, J. H. An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. NeuroImage 19, 1233–1239. 10.1016/s1053-8119(03)00169-1 (2003). DOI: 10.1016/s1053-8119(03)00169-1
Rigby, R. A. & Stasinopoulos, D. M. Generalized additive models for location, scale and shape. J. R. Stat. Soc. Ser. C (Appl. Stat.) 54, 507–554 (2005). DOI: 10.1111/j.1467-9876.2005.00510.x
Stasinopoulos, D. M. & Rigby, R. A. Generalized additive models for location scale and shape (GAMLSS) in R. J. Stat. Softw. 23, 1–46 (2008).
Nir, Y. et al. Regional slow waves and spindles in human sleep. Neuron 70, 153–169. 10.1016/j.neuron.2011.02.043 (2011). DOI: 10.1016/j.neuron.2011.02.043
Rigby, R. A. & Stasinopoulos, D. M. Automatic smoothing parameter selection in GAMLSS with an application to centile estimation. Stat. Methods Med. Res. 23, 318–332 (2014). DOI: 10.1177/0962280212473302
Stasinopoulos, M. D., Rigby, R. A., Heller, G. Z., Voudouris, V. & De Bastiani, F. Flexible Regression and Smoothing: Using GAMLSS in R (CRC Press, 2017). DOI: 10.1201/b21973
Rigby, R. A., Stasinopoulos, M. D., Heller, G. Z. & De Bastiani, F. Distributions for Modeling Location, Scale, and Shape: Using GAMLSS in R (CRC Press, 2019). DOI: 10.1201/9780429298547
van Buuren, S. & Fredriks, M. Worm plot: A simple diagnostic device for modelling growth reference curves. Stat. Med. 20, 1259–1277. 10.1002/sim.746 (2001). DOI: 10.1002/sim.746
Timmerman, M. E., Voncken, L. & Albers, C. J. A tutorial on regression-based norming of psychological tests with GAMLSS. Psychol. Methods 26, 357–373. 10.1037/met0000348 (2021). DOI: 10.1037/met0000348
Faul, F., Erdfelder, E., Lang, A. G. & Buchner, A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191. 10.3758/bf03193146 (2007). DOI: 10.3758/bf03193146
Faul, F., Erdfelder, E., Buchner, A. & Lang, A. G. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behav. Res. Methods 41, 1149–1160. 10.3758/BRM.41.4.1149 (2009). DOI: 10.3758/BRM.41.4.1149
Horne, J. A. & Ostberg, O. A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms. Int. J. Chronobiol. 4, 97–110 (1976).
