Regional patterns of human cortex development correlate with underlying neurobiology.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
12 Sep 2024
12 Sep 2024
Historique:
received:
09
11
2023
accepted:
29
08
2024
medline:
17
9
2024
pubmed:
17
9
2024
entrez:
16
9
2024
Statut:
epublish
Résumé
Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cortical thickness development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of the variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical thickness change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans.
Identifiants
pubmed: 39284858
doi: 10.1038/s41467-024-52366-7
pii: 10.1038/s41467-024-52366-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
7987Informations de copyright
© 2024. The Author(s).
Références
Bethlehem, R. A. I. et al. Brain charts for the human lifespan. Nature 604, 525–533 (2022).
pubmed: 35388223
pmcid: 9021021
doi: 10.1038/s41586-022-04554-y
Gogtay, N. et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc. Natl Acad. Sci. 101, 8174–8179 (2004).
pubmed: 15148381
pmcid: 419576
doi: 10.1073/pnas.0402680101
Rutherford, S. et al. Charting brain growth and aging at high spatial precision. eLife 11, e72904 (2022).
pubmed: 35101172
pmcid: 8828052
doi: 10.7554/eLife.72904
Vanderhaeghen, P. & Polleux, F. Developmental mechanisms underlying the evolution of human cortical circuits. Nat. Rev. Neurosci. 24, 213–232 (2023).
pubmed: 36792753
pmcid: 10064077
doi: 10.1038/s41583-023-00675-z
Erus, G. et al. Imaging patterns of brain development and their relationship to cognition. Cereb. Cortex 25, 1676–1684 (2015).
pubmed: 24421175
doi: 10.1093/cercor/bht425
Norbom, L. B. et al. New insights into the dynamic development of the cerebral cortex in childhood and adolescence: Integrating macro- and microstructural MRI findings. Prog. Neurobiol. 204, 102109 (2021).
pubmed: 34147583
doi: 10.1016/j.pneurobio.2021.102109
Paolicelli, R. C. et al. Synaptic pruning by microglia is necessary for normal brain development. Science 333, 1456–1458 (2011).
pubmed: 21778362
doi: 10.1126/science.1202529
Sowell, E. R. et al. Longitudinal mapping of cortical thickness and brain growth in normal children. J. Neurosci. 24, 8223–8231 (2004).
pubmed: 15385605
pmcid: 6729679
doi: 10.1523/JNEUROSCI.1798-04.2004
Walhovd, K. B., Fjell, A. M., Giedd, J., Dale, A. M. & Brown, T. T. Through Thick and Thin: a Need to Reconcile Contradictory Results on Trajectories in Human Cortical Development. Cereb. Cortex 27, bhv301 (2017).
pubmed: 28365755
Huttenlocher, P. R. Synaptic density in human frontal cortex - developmental changes and effects of aging. Brain Res 163, 195–205 (1979).
pubmed: 427544
doi: 10.1016/0006-8993(79)90349-4
Huttenlocher, P. R. Morphometric study of human cerebral cortex development. Neuropsychologia 28, 517–527 (1990).
pubmed: 2203993
doi: 10.1016/0028-3932(90)90031-I
Petanjek, Z., Judaš, M., Kostović, I. & Uylings, H. B. M. Lifespan alterations of basal dendritic trees of pyramidal neurons in the human prefrontal cortex: a layer-specific pattern. Cereb. Cortex 18, 915–929 (2008).
pubmed: 17652464
doi: 10.1093/cercor/bhm124
Petanjek, Z. et al. Extraordinary neoteny of synaptic spines in the human prefrontal cortex. Proc. Natl Acad. Sci. 108, 13281–13286 (2011).
pubmed: 21788513
pmcid: 3156171
doi: 10.1073/pnas.1105108108
Paus, T., Keshavan, M. & Giedd, J. N. Why do many psychiatric disorders emerge during adolescence? Nat. Rev. Neurosci. 9, 947–957 (2008).
pubmed: 19002191
pmcid: 2762785
doi: 10.1038/nrn2513
Li, H. et al. Laminar and columnar development of Barrel Cortex Relies on Thalamocortical neurotransmission. Neuron 79, 970–986 (2013).
pubmed: 24012009
pmcid: 3768017
doi: 10.1016/j.neuron.2013.06.043
Altamura, C. et al. Altered neocortical cell density and layer thickness in Serotonin transporter knockout mice: a quantitation study. Cereb. Cortex 17, 1394–1401 (2007).
pubmed: 16905592
doi: 10.1093/cercor/bhl051
Kalsbeek, A., Matthijssen, M. A. H. & Uylings, H. B. M. Morphometric analysis of prefrontal cortical development following neonatal lesioning of the dopaminergic mesocortical projection. Exp. Brain Res. 78, 279–289 (1989).
pubmed: 2599038
doi: 10.1007/BF00228899
Grewen, K. et al. Prenatal cocaine effects on brain structure in early infancy. NeuroImage 101, 114–123 (2014).
pubmed: 24999039
doi: 10.1016/j.neuroimage.2014.06.070
Bhide, P. G. Dopamine, cocaine and the development of cerebral cortical cytoarchitecture: A review of current concepts. Semin. Cell Dev. Biol. 20, 395–402 (2009).
pubmed: 19560044
doi: 10.1016/j.semcdb.2009.01.006
Ge, R. et al. Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation. Lancet Digit. Health 6, e211–e221 (2024).
pubmed: 38395541
pmcid: 10929064
doi: 10.1016/S2589-7500(23)00250-9
Marquand, A. F., Rezek, I., Buitelaar, J. & Beckmann, C. F. Understanding heterogeneity in clinical cohorts using normative models: beyond case-control studies. Biol. Psychiatry 80, 552–561 (2016).
pubmed: 26927419
pmcid: 5023321
doi: 10.1016/j.biopsych.2015.12.023
Bedford, S. A., Seidlitz, J. & Bethlehem, R. A. I. Translational potential of human brain charts. Clin. Transl. Med 12, e960 (2022).
pubmed: 35858047
pmcid: 9299572
doi: 10.1002/ctm2.960
Dukart, J. et al. JuSpace: A tool for spatial correlation analyses of magnetic resonance imaging data with nuclear imaging derived neurotransmitter maps. Hum. Brain Mapp. 42, 555–566 (2021).
pubmed: 33079453
doi: 10.1002/hbm.25244
Markello, R. D. et al. neuromaps: structural and functional interpretation of brain maps. Nat. Methods 1–8 https://doi.org/10.1038/s41592-022-01625-w (2022).
Hansen, J. Y. et al. Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nat. Neurosci. 25, 1569–1581 (2022).
pubmed: 36303070
pmcid: 9630096
doi: 10.1038/s41593-022-01186-3
Hansen, J. Y. et al. Local molecular and global connectomic contributions to cross-disorder cortical abnormalities. Nat. Commun. 13, 4682 (2022).
pubmed: 35948562
pmcid: 9365855
doi: 10.1038/s41467-022-32420-y
Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).
pubmed: 22031440
pmcid: 3566780
doi: 10.1038/nature10523
Zhu, Y. et al. Spatiotemporal transcriptomic divergence across human and macaque brain development. Science 362, eaat8077 (2018).
pubmed: 30545855
pmcid: 6900982
doi: 10.1126/science.aat8077
Siletti, K. et al. Transcriptomic diversity of cell types across the adult human brain. Science 382, eadd7046 (2023).
pubmed: 37824663
doi: 10.1126/science.add7046
Huttenlocher, P. R. & Dabholkar, A. S. Regional differences in synaptogenesis in human cerebral cortex. J. Comp. Neurol. 387, 167–178 (1997).
pubmed: 9336221
doi: 10.1002/(SICI)1096-9861(19971020)387:2<167::AID-CNE1>3.0.CO;2-Z
Parker, N. et al. Assessment of neurobiological mechanisms of cortical thinning during childhood and adolescence and their implications for psychiatric disorders. JAMA Psychiatry 77, 1127 (2020).
pubmed: 32584945
pmcid: 7301307
doi: 10.1001/jamapsychiatry.2020.1495
Vidal-Pineiro, D. et al. Cellular correlates of cortical thinning throughout the lifespan. Sci. Rep. 10, 21803 (2020).
pubmed: 33311571
pmcid: 7732849
doi: 10.1038/s41598-020-78471-3
Ball, G., Seidlitz, J., Beare, R. & Seal, M. L. Cortical remodelling in childhood is associated with genes enriched for neurodevelopmental disorders. NeuroImage 215, 116803 (2020).
pubmed: 32276068
doi: 10.1016/j.neuroimage.2020.116803
Patel, Y. et al. Maturation of the human cerebral cortex during adolescence: myelin or dendritic arbor? Cereb. Cortex 29, 3351–3362 (2019).
pubmed: 30169567
doi: 10.1093/cercor/bhy204
Shin, J. et al. Cell-specific gene-expression profiles and cortical thickness in the human brain. Cereb. Cortex 28, 3267–3277 (2018).
pubmed: 28968835
doi: 10.1093/cercor/bhx197
Whitaker, K. J. et al. Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome. Proc. Natl Acad. Sci. USA 113, 9105–9110 (2016).
pubmed: 27457931
pmcid: 4987797
doi: 10.1073/pnas.1601745113
Paus, T. Imaging microstructure in the living human brain: A viewpoint. NeuroImage 182, 3–7 (2018).
pubmed: 29024791
doi: 10.1016/j.neuroimage.2017.10.013
Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).
pubmed: 22996553
pmcid: 4243026
doi: 10.1038/nature11405
Liharska, L. E. et al. A study of gene expression in the living human brain. 2023.04.21.23288916 Preprint at https://doi.org/10.1101/2023.04.21.23288916 (2023).
Kasper, J. et al. Local synchronicity in dopamine-rich caudate nucleus influences Huntington’s disease motor phenotype. Brain 146, 3319–3330 (2023).
pubmed: 36795496
doi: 10.1093/brain/awad043
Premi, E. et al. Unravelling neurotransmitters impairment in primary progressive aphasias. Hum. Brain Mapp. 44, 2245–2253 (2023).
pubmed: 36649260
pmcid: 10028634
doi: 10.1002/hbm.26206
Lake, B. B. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016).
pubmed: 27339989
pmcid: 5038589
doi: 10.1126/science.aaf1204
Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015).
pubmed: 26060301
pmcid: 4466750
doi: 10.1073/pnas.1507125112
Kim, M.-J. et al. First-in-human evaluation of [11C]PS13, a novel PET radioligand, to quantify cyclooxygenase-1 in the brain. Eur. J. Nucl. Med Mol. Imaging 47, 3143–3151 (2020).
pubmed: 32399622
pmcid: 8261645
doi: 10.1007/s00259-020-04855-2
Lois, C. et al. Neuroinflammation in Huntington’s disease: new insights with
pubmed: 29719953
doi: 10.1021/acschemneuro.8b00072
Destrieux, C., Fischl, B., Dale, A. & Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 53, 1–15 (2010).
pubmed: 20547229
doi: 10.1016/j.neuroimage.2010.06.010
Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43–54 (2018).
pubmed: 29567376
pmcid: 5999559
doi: 10.1016/j.dcn.2018.03.001
Schumann, G. et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol. Psychiatry 15, 1128–1139 (2010).
pubmed: 21102431
doi: 10.1038/mp.2010.4
Douaud, G. et al. A common brain network links development, aging, and vulnerability to disease. Proc. Natl Acad. Sci. 111, 17648–17653 (2014).
pubmed: 25422429
pmcid: 4267352
doi: 10.1073/pnas.1410378111
Becht, A. I. & Mills, K. L. Modeling individual differences in brain development. Biol. Psychiatry 88, 63–69 (2020).
pubmed: 32245576
pmcid: 7305975
doi: 10.1016/j.biopsych.2020.01.027
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).
pubmed: 16530430
doi: 10.1016/j.neuroimage.2006.01.021
Azen, R. & Budescu, D. V. The dominance analysis approach for comparing predictors in multiple regression. Psychol. Methods 8, 129–148 (2003).
pubmed: 12924811
doi: 10.1037/1082-989X.8.2.129
Warrier, V. et al. Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes. Nat. Genet 55, 1483–1493 (2023).
pubmed: 37592024
pmcid: 10600728
doi: 10.1038/s41588-023-01475-y
Goyal, M. S., Hawrylycz, M., Miller, J. A., Snyder, A. Z. & Raichle, M. E. Aerobic glycolysis in the human brain is associated with development and neotenous gene expression. Cell Metab. 19, 49–57 (2014).
pubmed: 24411938
pmcid: 4389678
doi: 10.1016/j.cmet.2013.11.020
Pomponio, R. et al. Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. NeuroImage 208, 116450 (2020).
pubmed: 31821869
doi: 10.1016/j.neuroimage.2019.116450
Fortin, J.-P. et al. Harmonization of cortical thickness measurements across scanners and sites. NeuroImage 167, 104–120 (2018).
pubmed: 29155184
doi: 10.1016/j.neuroimage.2017.11.024
Yeung, M. S. Y. et al. Dynamics of Oligodendrocyte generation and myelination in the human brain. Cell 159, 766–774 (2014).
pubmed: 25417154
doi: 10.1016/j.cell.2014.10.011
Glantz, L. A., Gilmore, J. H., Hamer, R. M., Lieberman, J. A. & Jarskog, L. F. Synaptophysin and PSD-95 in the human prefrontal cortex from mid-gestation into early adulthood. Neuroscience 149, 582–591 (2007).
pubmed: 17916412
doi: 10.1016/j.neuroscience.2007.06.036
Fung, S. J. et al. Expression of interneuron markers in the dorsolateral prefrontal cortex of the developing human and in schizophrenia. AJP 167, 1479–1488 (2010).
doi: 10.1176/appi.ajp.2010.09060784
McNamara, N. B. et al. Microglia regulate central nervous system myelin growth and integrity. Nature 613, 120–129 (2023).
pubmed: 36517604
doi: 10.1038/s41586-022-05534-y
Santos, E. N. & Fields, R. D. Regulation of myelination by microglia. Sci. Adv. 7, eabk1131 (2021).
pubmed: 34890221
pmcid: 8664250
doi: 10.1126/sciadv.abk1131
Miller, D. J. et al. Prolonged myelination in human neocortical evolution. Proc. Natl Acad. Sci. USA 109, 16480–16485 (2012).
pubmed: 23012402
pmcid: 3478650
doi: 10.1073/pnas.1117943109
Grydeland, H., Walhovd, K. B., Tamnes, C. K., Westlye, L. T. & Fjell, A. M. Intracortical myelin links with performance variability across the human lifespan: results from T1- and T2-weighted MRI myelin mapping and diffusion tensor imaging. J. Neurosci. 33, 18618–18630 (2013).
pubmed: 24259583
pmcid: 6618798
doi: 10.1523/JNEUROSCI.2811-13.2013
Grydeland, H. et al. Waves of maturation and senescence in micro-structural MRI markers of human cortical myelination over the lifespan. Cereb. Cortex 29, 1369–1381 (2019).
pubmed: 30590439
doi: 10.1093/cercor/bhy330
Chugani, H. T., Phelps, M. E. & Mazziotta, J. C. Positron emission tomography study of human brain functional development. Ann. Neurol. 22, 487–497 (1987).
pubmed: 3501693
doi: 10.1002/ana.410220408
Takahashi, T., Shirane, R., Sato, S. & Yoshimoto, T. Developmental changes of cerebral blood flow and oxygen metabolism in children. AJNR Am. J. Neuroradiol. 20, 917–922 (1999).
pubmed: 10369366
pmcid: 7056161
Shan, Z. Y. et al. Cerebral glucose metabolism on positron emission tomography of children. Hum. Brain Mapp. 35, 2297–2309 (2013).
pubmed: 23897639
pmcid: 4084709
doi: 10.1002/hbm.22328
Satterthwaite, T. D. et al. Impact of puberty on the evolution of cerebral perfusion during adolescence. Proc. Natl Acad. Sci. 111, 8643–8648 (2014).
pubmed: 24912164
pmcid: 4060665
doi: 10.1073/pnas.1400178111
Weickert, C. S. et al. Postnatal alterations in dopaminergic markers in the human prefrontal cortex. Neuroscience 144, 1109–1119 (2007).
pubmed: 17123740
doi: 10.1016/j.neuroscience.2006.10.009
Jucaite, A., Forssberg, H., Karlsson, P., Halldin, C. & Farde, L. Age-related reduction in dopamine D1 receptors in the human brain: from late childhood to adulthood, a positron emission tomography study. Neuroscience 167, 104–110 (2010).
pubmed: 20109534
doi: 10.1016/j.neuroscience.2010.01.034
Johansson, J. et al. Biphasic patterns of age-related differences in dopamine D1 receptors across the adult lifespan. Cell Rep. 42, 113107 (2023).
Schliebs, R. & Arendt, T. The cholinergic system in aging and neuronal degeneration. Behav. Brain Res. 221, 555–563 (2011).
pubmed: 21145918
doi: 10.1016/j.bbr.2010.11.058
Liang, X. et al. Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence. Nat. Commun. 15, 784 (2024).
pubmed: 38278807
pmcid: 10817914
doi: 10.1038/s41467-024-44863-6
Henrich, J., Heine, S. J. & Norenzayan, A. The weirdest people in the world? Behav. Brain Sci. 33, 61–83 (2010).
pubmed: 20550733
doi: 10.1017/S0140525X0999152X
Wei, Y., Zhang, H. & Liu, Y. Charting normative brain variability across the human lifespan. Neurosci. Bull. 39, 362–364 (2023).
pubmed: 36129601
doi: 10.1007/s12264-022-00952-4
Wong, A. P.-Y. et al. Inter-regional variations in gene expression and age-related cortical thinning in the adolescent brain. Cereb. Cortex 28, 1272–1281 (2018).
pubmed: 28334178
doi: 10.1093/cercor/bhx040
Kasper, J. et al. Resting-State Changes in Aging and Parkinson’s Disease Are Shaped by Underlying Neurotransmission: A Normative Modeling Study. Biol. Psychiatry Cogn. Neurosci. Neuroimaging, in press https://doi.org/10.1016/j.bpsc.2024.04.010 (2024).
Harms, A. et al. Mesiotemporal volumetry, cortical thickness, and neuropsychological deficits in the long-term course of Limbic Encephalitis. Neurol. Neuroimmunol. Neuroinflamm. 10, e200125 (2023).
pubmed: 37230543
pmcid: 10211327
doi: 10.1212/NXI.0000000000200125
Bartels, F. et al. Clinical and magnetic resonance imaging outcome predictors in pediatric Anti–N-Methyl-D-Aspartate receptor Encephalitis. Ann. Neurol. 88, 148–159 (2020).
pubmed: 32314416
doi: 10.1002/ana.25754
Marquand, A. F. et al. Conceptualizing mental disorders as deviations from normative functioning. Mol. Psychiatry 24, 1415–1424 (2019).
pubmed: 31201374
pmcid: 6756106
doi: 10.1038/s41380-019-0441-1
Panizzon, M. S. et al. Distinct genetic influences on cortical surface area and cortical thickness. Cereb. Cortex 19, 2728–2735 (2009).
pubmed: 19299253
pmcid: 2758684
doi: 10.1093/cercor/bhp026
Wierenga, L. M., Langen, M., Oranje, B. & Durston, S. Unique developmental trajectories of cortical thickness and surface area. NeuroImage 87, 120–126 (2014).
pubmed: 24246495
doi: 10.1016/j.neuroimage.2013.11.010
Di Biase, M. A. et al. Mapping human brain charts cross-sectionally and longitudinally. Proc. Natl Acad. Sci. 120, e2216798120 (2023).
pubmed: 37155868
pmcid: 10193972
doi: 10.1073/pnas.2216798120
Collins, M. A. et al. Accelerated cortical thinning precedes and predicts conversion to psychosis: The NAPLS3 longitudinal study of youth at clinical high-risk. Mol. Psychiatry 28, 1182–1189 (2023).
pubmed: 36434057
doi: 10.1038/s41380-022-01870-7
Lotter, L. D. & Dukart, J. JuSpyce - a toolbox for flexible assessment of spatial associations between brain maps. Zenodo https://doi.org/10.5281/zenodo.6884932 (2022).
Markello, R. D. et al. Standardizing workflows in imaging transcriptomics with the abagen toolbox. eLife 10, e72129 (2021).
pubmed: 34783653
pmcid: 8660024
doi: 10.7554/eLife.72129
Burt, J. B., Helmer, M., Shinn, M., Anticevic, A. & Murray, J. D. Generative modeling of brain maps with spatial autocorrelation. NeuroImage 220, 117038 (2020).
pubmed: 32585343
doi: 10.1016/j.neuroimage.2020.117038
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
pubmed: 32015543
pmcid: 7056644
doi: 10.1038/s41592-019-0686-2
Salo, T. et al. neurostuff/NiMARE: 0.0.9rc2. Zenodo https://doi.org/10.5281/zenodo.4895600 (2021).
Hunter, J. D. Matplotlib: A 2D Graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
doi: 10.1109/MCSE.2007.55
Waskom, M. L. seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).
doi: 10.21105/joss.03021
Gale, D. J., Vos, de Wael, R., Benkarim, O. & Bernhardt, B. Surfplot: Publication-ready brain surface figures. Zenodo https://doi.org/10.5281/zenodo.5567926 (2021).
doi: 10.5281/zenodo.5567926
Marquand, A. F. et al. PCNToolkit. Zenodo https://doi.org/10.5281/zenodo.5207839 (2021).
Rutherford, S. et al. The Normative Modeling Framework for Computational Psychiatry. https://www.biorxiv.org/content/10.1101/2021.08.08.455583v1 (2021).
Wey, H.-Y. et al. Insights into neuroepigenetics through human histone deacetylase PET imaging. Sci. Transl. Med. 8, 351ra106 (2016).
pubmed: 27510902
pmcid: 5784409
doi: 10.1126/scitranslmed.aaf7551
Kaulen, N. et al. mGluR5 and GABAA receptor-specific parametric PET atlas construction—PET/MR data processing pipeline, validation, and application. Hum. Brain Mapp. 43, 2148–2163 (2022).
pubmed: 35076125
pmcid: 8996359
doi: 10.1002/hbm.25778
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
pubmed: 27437579
pmcid: 4990127
doi: 10.1038/nature18933
Wu, J. et al. Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems. Hum. Brain Mapp. 39, 3793–3808 (2018).
pubmed: 29770530
pmcid: 6239990
doi: 10.1002/hbm.24213
Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).
pubmed: 30545857
pmcid: 6413328
doi: 10.1126/science.aat8464
Fischl, B. FreeSurfer. Neuroimage 62, 774–781 (2012).
pubmed: 22248573
doi: 10.1016/j.neuroimage.2012.01.021
Monereo-Sánchez, J. et al. Quality control strategies for brain MRI segmentation and parcellation: Practical approaches and recommendations - insights from the Maastricht study. NeuroImage 237, 118174 (2021).
pubmed: 34000406
doi: 10.1016/j.neuroimage.2021.118174
Fraza, C. J., Dinga, R., Beckmann, C. F. & Marquand, A. F. Warped Bayesian linear regression for normative modelling of big data. NeuroImage 245, 118715 (2021).
pubmed: 34798518
doi: 10.1016/j.neuroimage.2021.118715
Bayer, J. M. M. et al. Accommodating Site Variation in Neuroimaging Data Using Normative and Hierarchical Bayesian Models. https://www.biorxiv.org/content/10.1101/2021.02.09.430363v2 (2021).
Kia, S. M. et al. Federated Multi-Site Normative Modeling Using Hierarchical Bayesian Regression. 2021.05.28.446120 https://www.biorxiv.org/content/10.1101/2021.05.28.446120v1 (2021).
Rosen, A. F. G. et al. Quantitative assessment of structural image quality. NeuroImage 169, 407–418 (2018).
pubmed: 29278774
doi: 10.1016/j.neuroimage.2017.12.059
Dukart, J. et al. Cerebral blood flow predicts differential neurotransmitter activity. Sci. Rep. 8, 4074 (2018).
pubmed: 29511260
pmcid: 5840131
doi: 10.1038/s41598-018-22444-0
Markello, R. D. & Misic, B. Comparing spatial null models for brain maps. NeuroImage 236, 118052 (2021).
pubmed: 33857618
doi: 10.1016/j.neuroimage.2021.118052
Lotter, L. D. Repository: Regional patterns of human cortex development correlate with underlying neurobiology. Zenodo https://doi.org/10.5281/zenodo.7901282 (2024).
Pantano, P. et al. Regional cerebral blood flow and oxygen consumption in human aging. Stroke 15, 635–641 (1984).
pubmed: 6611613
doi: 10.1161/01.STR.15.4.635
Yamaguchi, T. et al. Reduction in regional cerebral metabolic rate of oxygen during human aging. Stroke 17, 1220–1228 (1986).
pubmed: 3492786
doi: 10.1161/01.STR.17.6.1220
Nordberg, A., Alafuzoff, I. & Winblad, B. Nicotinic and muscarinic subtypes in the human brain: Changes with aging and dementia. J. Neurosci. Res. 31, 103–111 (1992).
pubmed: 1613816
doi: 10.1002/jnr.490310115
Mesulam, M.-M. & Geula, C. Acetylcholinesterase-rich pyramidal neurons in the human neocortex and hippocampus: Absence at birth, development during the life span, and dissolution in Alzheimer’s disease. Ann. Neurol. 24, 765–773 (1988).
pubmed: 3207359
doi: 10.1002/ana.410240611