Wittchen, H. U. et al. The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur. Neuropsychopharmacol. 21, 655–679 (2011).
Google Scholar
van Someren, E. J. W. Brain mechanisms of insomnia: new perspectives on causes and consequences. Physiol. Rev. 101, 995–1046 (2021).
Google Scholar
Cuijpers, P. The challenges of improving treatments for depression. JAMA 320, 2529–2530 (2018).
Google Scholar
Tyrer, P. & Baldwin, D. Generalised anxiety disorder. Lancet 368, 2156–2166 (2006).
Google Scholar
Schiel, J. E. et al. Associations between sleep health and grey matter volume in the UK Biobank cohort (n = 33 356). Brain Commun. 5, fcad200 (2023).
Google Scholar
Schmaal, L. et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol. Psychiatry 22, 900–909 (2017).
Google Scholar
Harrewijn, A. et al. Cortical and subcortical brain structure in generalized anxiety disorder: findings from 28 research sites in the ENIGMA-Anxiety Working Group. Transl. Psychiatry 11, 502 (2021).
Google Scholar
Jansen, P. R. et al. Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways. Nat. Genet. 51, 394–403 (2019).
Google Scholar
Romero, C. et al. Exploring the genetic overlap between twelve psychiatric disorders. Nat. Genet. 54, 1795–1802 (2022).
Google Scholar
Ohayon, M. M. & Roth, T. Place of chronic insomnia in the course of depressive and anxiety disorders. J. Psychiatr. Res. 37, 9–15 (2003).
Google Scholar
Soehner, A. M. & Harvey, A. G. Prevalence and functional consequences of severe insomnia symptoms in mood and anxiety disorders: results from a nationally representative sample. Sleep 35, 1367–1375 (2012).
Google Scholar
Serra-Blasco, M. et al. Structural brain correlates in major depression, anxiety disorders and post-traumatic stress disorder: a voxel-based morphometry meta-analysis. Neurosci. Biobehav. Rev. 129, 269–281 (2021).
Google Scholar
Goodkind, M. et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry 72, 305–315 (2015).
Google Scholar
Janiri, D. et al. Shared neural phenotypes for mood and anxiety disorders: a meta-analysis of 226 task-related functional imaging studies. JAMA Psychiatry 77, 172–179 (2020).
Google Scholar
Abdelhack, M. et al. Opposing brain signatures of sleep in task-based and resting-state conditions. Nat. Commun. 14, 7927 (2023).
Google Scholar
Leerssen, J. et al. Brain structural correlates of insomnia severity in 1053 individuals with major depressive disorder: results from the ENIGMA MDD Working Group. Transl. Psychiatry 10, 425 (2020).
Google Scholar
Benson, K. L., Winkelman, J. W. & Gönenç, A. Disrupted white matter integrity in primary insomnia and major depressive disorder: relationships to sleep quality and depression severity. J. Sleep Res. 32, e13913 (2023).
Google Scholar
Bresser, T. et al. The role of brain white matter in depression resilience and response to sleep interventions. Brain Commun. 5, fcad210 (2023).
Google Scholar
Li, C. et al. Dynamic functional abnormalities in generalized anxiety disorders and their increased network segregation of a hyperarousal brain state modulated by insomnia. J. Affect. Disord. 246, 338–345 (2019).
Google Scholar
Pace-Schott, E. F. et al. Resting state functional connectivity in primary insomnia, generalized anxiety disorder and controls. Psychiatry Res. Neuroimaging 265, 26–34 (2017).
Google Scholar
Shen, Z. et al. Deficits in brain default mode network connectivity mediate the relationship between poor sleep quality and anxiety severity. Sleep 47, zsad296 (2023).
Google Scholar
Li, M. et al. Abnormalities of thalamus volume and resting state functional connectivity in primary insomnia patients. Brain Imaging Behav. 13, 1193–1201 (2019).
Google Scholar
Nugent, A. C., Davis, R. M., Zarate, C. A. Jr & Drevets, W. C. Reduced thalamic volumes in major depressive disorder. Psychiatry Res. 213, 179–185 (2013).
Google Scholar
McCutcheon, R. A. et al. Shared and separate patterns in brain morphometry across transdiagnostic dimensions. Nat. Mental Health 1, 55–65 (2023).
Google Scholar
van Erp, T. G. et al. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol. Psychiatry 21, 547–553 (2016).
Google Scholar
Hibar, D. P. et al. Subcortical volumetric abnormalities in bipolar disorder. Mol. Psychiatry 21, 1710–1716 (2016).
Google Scholar
Stolicyn, A. et al. Comprehensive assessment of sleep duration, insomnia, and brain structure within the UK Biobank cohort. Sleep 47, zsad274 (2023).
Google Scholar
Jespersen, K. V. et al. Reduced structural connectivity in insomnia disorder. J. Sleep Res. 29, e12901 (2020).
Google Scholar
Tamm, S. et al. No association between amygdala responses to negative faces and depressive symptoms: cross-sectional data from 28,638 individuals in the UK Biobank cohort. Am. J. Psychiatry 179, 509–513 (2022).
Google Scholar
Schiel, J. E. et al. Associations between sleep health and amygdala reactivity to negative facial expressions in the UK Biobank cohort. Biol. Psychiatry 92, 693–700 (2022).
Google Scholar
Calhoun, V. D. & Sui, J. Multimodal fusion of brain imaging data: a key to finding the missing link(s) in complex mental illness. Biol. Psychiatry 1, 230–244 (2016).
McEwen, B. S., Nasca, C. & Gray, J. D. Stress effects on neuronal structure: hippocampus, amygdala, and prefrontal cortex. Neuropsychopharmacology 41, 3–23 (2016).
Google Scholar
Genzel, L., Spoormaker, V. I., Konrad, B. N. & Dresler, M. The role of rapid eye movement sleep for amygdala-related memory processing. Neurobiol. Learn. Mem. 122, 110–121 (2015).
Google Scholar
Wassing, R. et al. Restless REM sleep impedes overnight amygdala adaptation. Curr. Biol. 29, 2351–2358.e4 (2019).
Google Scholar
Cabrera, Y. et al. Overnight neuronal plasticity and adaptation to emotional distress. Nat. Rev. Neurosci. 25, 253–271 (2024).
Google Scholar
Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654–660 (2022).
Google Scholar
Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).
Google Scholar
Nakua, H. et al. Systematic comparisons of different quality control approaches applied to three large pediatric neuroimaging datasets. NeuroImage 274, 120119 (2023).
Google Scholar
Helwegen, K., Libedinsky, I. & van den Heuvel, M. P. Statistical power in network neuroscience. Trends Cogn. Sci. 27, 282–301 (2023).
Google Scholar
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
Google Scholar
Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).
Google Scholar
Smith, S. M., Alfaro-Almagro, F. & Miller, K. L. UK Biobank Brain Imaging Documentation (Oxford Centre for Functional MRI of the Brain (FMRIB/WIN), Oxford University on behalf of UK Biobank, accessed 30 January 2024); https://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/brain_mri.pdf.
Fischl, B. FreeSurfer. NeuroImage 62, 774–781 (2012).
Google Scholar
Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968–980 (2006).
Google Scholar
de Lange, S. C., Helwegen, K. & van den Heuvel, M. P. Structural and functional connectivity reconstruction with CATO—a Connectivity Analysis TOolbox. NeuroImage 273, 120108 (2023).
Google Scholar
Greve, D. N. & Fischl, B. Accurate and robust brain image alignment using boundary-based registration. NeuroImage 48, 63–72 (2009).
Google Scholar
Chang, L. C., Walker, L. & Pierpaoli, C. Informed RESTORE: a method for robust estimation of diffusion tensor from low redundancy datasets in the presence of physiological noise artifacts. Magn. Reson. Med. 68, 1654–1663 (2012).
Google Scholar
de Reus, M. A. An Eccentric Perspective on Brain Networks (Uitgeverij BOXPress, 2015).
Mori, S., Crain, B. J., Chacko, V. P. & van Zijl, P. C. M. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45, 265–269 (1999).
Google Scholar
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59, 2142–2154 (2012).
Google Scholar
Hariri, A. R., Tessitore, A., Mattay, V. S., Fera, F. & Weinberger, D. R. The amygdala response to emotional stimuli: a comparison of faces and scenes. NeuroImage 17, 317–323 (2002).
Google Scholar
Qian, Y. et al. Observational and genetic evidence highlight the association of human sleep behaviors with the incidence of fracture. Commun. Biol. 4, 1339 (2021).
Google Scholar
Kroenke, K., Spitzer, R. L. & Williams, J. B. The Patient Health Questionnaire-2: validity of a two-item depression screener. Med. Care 41, 1284–1292 (2003).
Google Scholar
Weiss, A. & Deary, I. J. A new look at neuroticism: should we worry so much about worrying? Curr. Dir. Psychol. Sci. 29, 92–101 (2020).
Google Scholar
Weiss, A. et al. Conditioning on a collider may or may not explain the relationship between lower neuroticism and premature mortality in the study by Gale et al. (2017): a reply to Richardson, Davey Smith, and Munafò (2019). Psychol. Sci. 30, 633–638 (2019).
Google Scholar
Eysenck, S. B., Eysenck, H. J. & Barrett, P. A revised version of the psychoticism scale. Pers. Individ. Differ. 6, 21–29 (1985).
Google Scholar
Alfaro-Almagro, F. et al. Confound modelling in UK Biobank brain imaging. NeuroImage 224, 117002 (2021).
Google Scholar
Nichols, T. E. & Holmes, A. P. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 15, 1–25 (2002).
Google Scholar
Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M. & Nichols, T. E. Permutation inference for the general linear model. NeuroImage 92, 381–397 (2014).
Google Scholar
Manly, B. F. Randomization, Bootstrap and Monte Carlo Methods in Biology (Chapman and Hall/CRC, 2007).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Google Scholar
Beam, E., Potts, C., Poldrack, R. A. & Etkin, A. A data-driven framework for mapping domains of human neurobiology. Nat. Neurosci. 24, 1733–1744 (2021).
Google Scholar
Hansen, J. Y. et al. Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nat. Neurosci. 25, 1569–1581 (2022).
Google Scholar
Cammoun, L. et al. Mapping the human connectome at multiple scales with diffusion spectrum MRI. J. Neurosci. Methods 203, 386–397 (2012).
Google Scholar
Scholtens, L. H., de Lange, S. C. & van den Heuvel, M. P. Simple brain plot (v1.0.0). Zenodo (2021).
link

+ There are no comments
Add yours