Association of polygenic scores for autism with volumetric MRI phenotypes in cerebellum and brainstem in adults.
Humans
Cerebellum
/ diagnostic imaging
Brain Stem
/ diagnostic imaging
Magnetic Resonance Imaging
Male
Female
Phenotype
Adult
Multifactorial Inheritance
Genetic Predisposition to Disease
Organ Size
Middle Aged
Autistic Disorder
/ genetics
Genome-Wide Association Study
Autism Spectrum Disorder
/ genetics
Gray Matter
/ diagnostic imaging
Case-Control Studies
Autism
Brain MRI
Brainstem
Cerebellum
Polygenic risk score
Journal
Molecular autism
ISSN: 2040-2392
Titre abrégé: Mol Autism
Pays: England
ID NLM: 101534222
Informations de publication
Date de publication:
07 Aug 2024
07 Aug 2024
Historique:
received:
10
05
2024
accepted:
22
07
2024
medline:
8
8
2024
pubmed:
8
8
2024
entrez:
7
8
2024
Statut:
epublish
Résumé
Previous research on autism spectrum disorders (ASD) have showed important volumetric alterations in the cerebellum and brainstem. Most of these studies are however limited to case-control studies with small clinical samples and including mainly children or adolescents. Herein, we aimed to explore the association between the cumulative genetic load (polygenic risk score, PRS) for ASD and volumetric alterations in the cerebellum and brainstem, as well as global brain tissue volumes of the brain among adults at the population level. We utilized the latest genome-wide association study of ASD by the Psychiatric Genetics Consortium (18,381 cases, 27,969 controls) and constructed the ASD PRS in an independent cohort, the UK Biobank. Regression analyses controlled for multiple comparisons with the false-discovery rate (FDR) at 5% were performed to investigate the association between ASD PRS and forty-four brain magnetic resonance imaging (MRI) phenotypes among ~ 31,000 participants. Primary analyses included sixteen MRI phenotypes: total volumes of the brain, cerebrospinal fluid (CSF), grey matter (GM), white matter (WM), GM of whole cerebellum, brainstem, and ten regions of the cerebellum (I_IV, V, VI, VIIb, VIIIa, VIIIb, IX, X, CrusI and CrusII). Secondary analyses included twenty-eight MRI phenotypes: the sub-regional volumes of cerebellum including the GM of the vermis and both left and right lobules of each cerebellar region. ASD PRS were significantly associated with the volumes of seven brain areas, whereby higher PRS were associated to reduced volumes of the whole brain, WM, brainstem, and cerebellar regions I-IV, IX, and X, and an increased volume of the CSF. Three sub-regional volumes including the left cerebellar lobule I-IV, cerebellar vermes VIIIb, and X were significantly and negatively associated with ASD PRS. The study highlights a substantial connection between susceptibility to ASD, its underlying genetic etiology, and neuroanatomical alterations of the adult brain.
Identifiants
pubmed: 39113134
doi: 10.1186/s13229-024-00611-7
pii: 10.1186/s13229-024-00611-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
34Subventions
Organisme : Prime Minister's Fellowship, People's Republic Government of Bangladesh
ID : PhD19B1013
Organisme : Svenska Sällskapet för Medicinsk Forskning,Sweden
ID : SSMF 30072019
Informations de copyright
© 2024. The Author(s).
Références
Diagnostic and statistical manual of mental disorders: DSM-5™, 5th ed (pp. xliv, 947). (2013). American Psychiatric Publishing, Inc. https://doi.org/10.1176/appi.books.9780890425596 .
Chen R, Jiao Y, Herskovits EH. Structural MRI in Autism Spectrum Disorder. Pediatr Res. 2011;69(8):63–8. https://doi.org/10.1203/PDR.0b013e318212c2b3 .
doi: 10.1203/PDR.0b013e318212c2b3
Faizo NL. A narrative review of MRI changes correlated to signs and symptoms of autism. Medicine. 2022;101(34):e30059. https://doi.org/10.1097/MD.0000000000030059 .
doi: 10.1097/MD.0000000000030059
pubmed: 36042586
pmcid: 9410622
Rafiee F, Rezvani Habibabadi R, Motaghi M, Yousem DM, Yousem IJ. Brain MRI in Autism Spectrum Disorder: Narrative Review and recent advances. J Magn Reson Imaging. 2022;55(6):1613–24. https://doi.org/10.1002/jmri.27949 .
doi: 10.1002/jmri.27949
pubmed: 34626442
Fatemi SH, Aldinger KA, Ashwood P, Bauman ML, Blaha CD, Blatt GJ, Chauhan A, Chauhan V, Dager SR, Dickson PE, Estes AM, Goldowitz D, Heck DH, Kemper TL, King BH, Martin LA, Millen KJ, Mittleman G, Mosconi MW, … Welsh JP. Consensus paper: Pathological role of the cerebellum in autism. The Cerebellum. 2012;11(3):777–807. https://doi.org/10.1007/s12311-012-0355-9 .
Wang SS-H, Kloth AD, Badura A. The Cerebellum, sensitive periods, and Autism. Neuron. 2014;83(3):518–32. https://doi.org/10.1016/j.neuron.2014.07.016 .
doi: 10.1016/j.neuron.2014.07.016
pubmed: 25102558
pmcid: 4135479
Willsey AJ, Sanders SJ, Li M, Dong S, Tebbenkamp AT, Muhle RA, Reilly SK, Lin L, Fertuzinhos S, Miller JA, Murtha MT, Bichsel C, Niu W, Cotney J, Ercan-Sencicek AG, Gockley J, Gupta AR, Han W, He X, … State MW. (2013). Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155(5): 997–1007. https://doi.org/10.1016/j.cell.2013.10.020 .
D’Mello AM, Crocetti D, Mostofsky SH, Stoodley CJ. Cerebellar gray matter and lobular volumes correlate with core autism symptoms. NeuroImage: Clin. 2015;7:631–9. https://doi.org/10.1016/j.nicl.2015.02.007 .
doi: 10.1016/j.nicl.2015.02.007
pubmed: 25844317
Dadalko OI, Travers BG. Evidence for Brainstem contributions to Autism Spectrum disorders. Front Integr Nuerosci. 2018;12. https://doi.org/10.3389/fnint.2018.00047 .
Seif A, Shea C, Schmid S, Stevenson R. A. A systematic review of Brainstem contributions to Autism Spectrum Disorder. Front Integr Nuerosci. 2021;15. https://doi.org/10.3389/fnint.2021.760116 .
Genovese A, Butler MG. The Autism Spectrum: behavioral, Psychiatric and Genetic associations. Genes. 2023;14(3). https://doi.org/10.3390/genes14030677 .
Hashem S, Nisar S, Bhat AA, Yadav SK, Azeem MW, Bagga P, Fakhro K, Reddy R, Frenneaux MP, Haris M. Genetics of structural and functional brain changes in autism spectrum disorder. Translational Psychiatry. 2020;10(1):1–17. https://doi.org/10.1038/s41398-020-00921-3 .
doi: 10.1038/s41398-020-00921-3
Pretzsch CM, Ecker C. Structural neuroimaging phenotypes and associated molecular and genomic underpinnings in autism: a review. Front NeuroSci. 2023;17. https://doi.org/10.3389/fnins.2023.1172779 .
Nisar S, Haris M. Neuroimaging genetics approaches to identify new biomarkers for the early diagnosis of autism spectrum disorder. Mol Psychiatry. 2023;1–14. https://doi.org/10.1038/s41380-023-02060-9 .
Kainer D, Templeton AR, Prates ET, Jacboson D, Allan ERO, Climer S, Garvin MR. Structural variants identified using non-mendelian inheritance patterns advance the mechanistic understanding of autism spectrum disorder. Hum Genet Genomics Adv. 2023;4(1):100150. https://doi.org/10.1016/j.xhgg.2022.100150 .
doi: 10.1016/j.xhgg.2022.100150
Grove J, Ripke S, Als TD, Mattheisen M, Walters RK, Won H, Pallesen J, Agerbo E, Andreassen OA, Anney R, Awashti S, Belliveau R, Bettella F, Buxbaum JD, Bybjerg-Grauholm J, Bækvad-Hansen M, Cerrato F, Chambert K, Christensen JH, … Børglum AD. (2019). Identification of common genetic risk variants for autism spectrum disorder. Nature Genetics 51(3): 431–444. https://doi.org/10.1038/s41588-019-0344-8 .
Martin AR, Daly MJ, Robinson EB, Hyman SE, Neale BM. Predicting Polygenic Risk of Psychiatric disorders. Mol Mech Affect Disturb. 2019;86(2):97–109. https://doi.org/10.1016/j.biopsych.2018.12.015 .
doi: 10.1016/j.biopsych.2018.12.015
Khundrakpam B, Vainik U, Gong J, Al-Sharif N, Bhutani N, Kiar G, Zeighami Y, Kirschner M, Luo C, Dagher A, Evans A. Neural correlates of polygenic risk score for autism spectrum disorders in general population. Brain Commun. 2020;2(2):fcaa092. https://doi.org/10.1093/braincomms/fcaa092 .
doi: 10.1093/braincomms/fcaa092
pubmed: 32954337
pmcid: 7475696
Alemany S, Blok E, Jansen PR, Muetzel RL, White T. Brain morphology, autistic traits, and polygenic risk for autism: a population-based neuroimaging study. Autism Res. 2021;14(10):2085–99. https://doi.org/10.1002/aur.2576 .
doi: 10.1002/aur.2576
pubmed: 34309210
Ranlund S, Rosa MJ, de Jong S, Cole JH, Kyriakopoulos M, Fu CHY, Mehta MA, Dima D. Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition. NeuroImage: Clin. 2018;20:1026–36. https://doi.org/10.1016/j.nicl.2018.10.008 .
doi: 10.1016/j.nicl.2018.10.008
pubmed: 30340201
Traut N, Beggiato A, Bourgeron T, Delorme R, Rondi-Reig L, Paradis A-L, Toro R. Cerebellar volume in Autism: literature Meta-analysis and analysis of the Autism Brain Imaging Data Exchange Cohort. Social Behav Autism. 2018;83(7):579–88. https://doi.org/10.1016/j.biopsych.2017.09.029 .
doi: 10.1016/j.biopsych.2017.09.029
Durkut M, Blok E, Suleri A, White T. The longitudinal bidirectional relationship between autistic traits and brain morphology from childhood to adolescence: a population-based cohort study. Mol Autism. 2022;13(1):31. https://doi.org/10.1186/s13229-022-00504-7 .
doi: 10.1186/s13229-022-00504-7
pubmed: 35790991
pmcid: 9258195
Liu J, Yao L, Zhang W, Xiao Y, Liu L, Gao X, Shah C, Li S, Tao B, Gong Q, Lui S. Gray Matter abnormalities in pediatric autism spectrum disorder: a meta-analysis with signed differential mapping. European Child Adolescent Psychiatry. 2017;26(8):933–45. https://doi.org/10.1007/s00787-017-0964-4 .
doi: 10.1007/s00787-017-0964-4
pubmed: 28233073
Yang Q, Huang P, Li C, Fang P, Zhao N, Nan J, Wang B, Gao W, Cui L-B. Mapping alterations of gray matter volume and white matter integrity in children with autism spectrum disorder: evidence from fMRI findings. NeuroReport. 2018;29(14):1188–92. https://doi.org/10.1097/WNR.0000000000001094 .
doi: 10.1097/WNR.0000000000001094
pubmed: 30001226
Pagnozzi AM, Conti E, Calderoni S, Fripp J, Rose SE. A systematic review of structural MRI biomarkers in autism spectrum disorder: a machine learning perspective. Int J Dev Neurosci. 2018;71(1):68–82. https://doi.org/10.1016/j.ijdevneu.2018.08.010 .
doi: 10.1016/j.ijdevneu.2018.08.010
pubmed: 30172895
Webb SJ, Sparks B-F, Friedman SD, Shaw DWW, Giedd J, Dawson G, Dager SR. Cerebellar vermal volumes and behavioral correlates in children with autism spectrum disorder. Psychiatry Res: Neuroimaging. 2009;172(1):61–7. https://doi.org/10.1016/j.pscychresns.2008.06.001 .
doi: 10.1016/j.pscychresns.2008.06.001
Laidi C, Floris DL, Tillmann J, Elandaloussi Y, Zabihi M, Charman T, Wolfers T, Durston S, Moessnang C, Dell’Acqua F, Ecker C, Loth E, Murphy D, Baron-Cohen S, Buitelaar JK, Marquand AF, Beckmann CF, Frouin V, Leboyer M, … Simonoff E. Cerebellar atypicalities in autism? Brain Development and Communication in Autism Spectrum Disorder 2022;92 (8): 674–682. https://doi.org/10.1016/j.biopsych.2022.05.020 .
Fernandez L, Burmester A, Duque JD, Silk TJ, Hyde CE, Kirkovski M, Enticott PG, Caeyenberghs K. Examination of cerebellar Grey-Matter volume in children with neurodevelopmental disorders: a coordinated analysis using the ACAPULCO Algorithm. Cerebellum. 2023;22(6):1243–9. https://doi.org/10.1007/s12311-022-01503-3 .
doi: 10.1007/s12311-022-01503-3
pubmed: 36482028
Courchesne E, Campbell K, Solso S. Brain growth across the life span in autism: age-specific changes in anatomical pathology. Brain Res. 2011;1380:138–45. https://doi.org/10.1016/j.brainres.2010.09.101 .
doi: 10.1016/j.brainres.2010.09.101
pubmed: 20920490
Duerden EG, Mak-Fan KM, Taylor MJ, Roberts SW. Regional differences in grey and white matter in children and adults with autism spectrum disorders: an activation likelihood estimate (ALE) meta-analysis. Autism Res. 2012;5(1):49–66. https://doi.org/10.1002/aur.235 .
doi: 10.1002/aur.235
pubmed: 22139976
Ecker C, Bookheimer SY, Murphy DGM. Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan. Lancet Neurol. 2015;14(11):1121–34. https://doi.org/10.1016/S1474-4422(15)00050-2 .
doi: 10.1016/S1474-4422(15)00050-2
pubmed: 25891007
Yang X, Si T, Gong Q, Qiu L, Jia Z, Zhou M, Zhao Y, Hu X, Wu M, Zhu H. Brain gray matter alterations and associated demographic profiles in adults with autism spectrum disorder: a meta-analysis of voxel-based morphometry studies. Australian New Z J Psychiatry. 2016;50(8):741–53. https://doi.org/10.1177/0004867415623858 .
doi: 10.1177/0004867415623858
Lange N, Travers BG, Bigler ED, Prigge MBD, Froehlich AL, Nielsen JA, Cariello AN, Zielinski BA, Anderson JS, Fletcher PT, Alexander AA, Lainhart JE. Longitudinal volumetric brain changes in Autism Spectrum Disorder ages 6–35 years. Autism Res. 2015;8(1):82–93. https://doi.org/10.1002/aur.1427 .
doi: 10.1002/aur.1427
pubmed: 25381736
Robinson EB, Koenen KC, McCormick MC, Munir K, Hallett V, Happé F, Plomin R, Ronald A. Evidence that autistic traits show the same etiology in the General Population and at the quantitative extremes (5%, 2.5%, and 1%). Arch Gen Psychiatry. 2011;68(11):1113–21. https://doi.org/10.1001/archgenpsychiatry.2011.119 .
doi: 10.1001/archgenpsychiatry.2011.119
pubmed: 22065527
pmcid: 3708488
Robinson EB, St Pourcain B, Anttila V, Kosmicki JA, Bulik-Sullivan B, Grove J, Maller J, Samocha KE, Sanders SJ, Ripke S, Martin J, Hollegaard MV, Werge T, Hougaard DM, Neale BM, Evans DM, Skuse D, Mortensen PB, Børglum AD, … Daly MJ. Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat Genet 2016;48(5):552–555. https://doi.org/10.1038/ng.3529 .
Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Downey P, Elliott P, Green J, Landray M, Liu B, Matthews P, Ong G, Pell J, Silman A, Young A, Sprosen T, Peakman T, Collins R. UK Biobank: an Open Access Resource for identifying the causes of a wide range of Complex diseases of Middle and Old Age. PLoS Med. 2015;12(3):e1001779. https://doi.org/10.1371/journal.pmed.1001779 .
doi: 10.1371/journal.pmed.1001779
pubmed: 25826379
pmcid: 4380465
Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51(4):584–91. https://doi.org/10.1038/s41588-019-0379-x .
doi: 10.1038/s41588-019-0379-x
pubmed: 30926966
pmcid: 6563838
Marees AT, de Kluiver H, Stringer S, Vorspan F, Curis E, Marie-Claire C, Derks EM. A tutorial on conducting genome-wide association studies: quality control and statistical analysis. Int J Methods Psychiatr Res. 2018;27(2):e1608. https://doi.org/10.1002/mpr.1608 .
doi: 10.1002/mpr.1608
pubmed: 29484742
pmcid: 6001694
Shang X, Zhang X, Huang Y, Zhu Z, Zhang X, Liu J, Wang W, Tang S, Yu H, Ge Z, Yang X, He M. Association of a wide range of individual chronic diseases and their multimorbidity with brain volumes in the UK Biobank: a cross-sectional study. eClinicalMedicine. 2022;47:101413. https://doi.org/10.1016/j.eclinm.2022.101413 .
doi: 10.1016/j.eclinm.2022.101413
pubmed: 35518119
pmcid: 9065617
Cox SR, Lyall DM, Ritchie SJ, Bastin ME, Harris MA, Buchanan CR, Fawns-Ritchie C, Barbu MC, de Nooij L, Reus LM, Alloza C, Shen X, Neilson E, Alderson HL, Hunter S, Liewald DC, Whalley HC, McIntosh AM, Lawrie SM, … Deary IJ. Associations between vascular risk factors and brain MRI indices in UK Biobank. Eur Heart J. 2019;40(28):2290–2300. https://doi.org/10.1093/eurheartj/ehz100 .
Alfaro-Almagro F, Jenkinson M, Bangerter NK, Andersson JLR, Griffanti L, Douaud G, Sotiropoulos SN, Jbabdi S, Hernandez-Fernandez M, Vallee E, Vidaurre D, Webster M, McCarthy P, Rorden C, Daducci A, Alexander DC, Zhang H, Dragonu I, Matthews PM, … Smith SM. Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. NeuroImage 2018;166:400–424. https://doi.org/10.1016/j.neuroimage.2017.10.034 .
Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, Xu J, Bartsch AJ, Jbabdi S, Sotiropoulos SN, Andersson JLR, Griffanti L, Douaud G, Okell TW, Weale P, Dragonu I, Garratt S, Hudson S, Collins R, Jenkinson M, … Smith SM. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci. 2016;19(11):1523–1536. https://doi.org/10.1038/nn.4393 .
Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imag. 2001;20(1):45–57. https://doi.org/10.1109/42.906424 .
Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A, Vukcevic D, Delaneau O, O’Connell J, Cortes A, Welsh S, Young A, Effingham M, McVean G, Leslie S, Allen N, Donnelly P, Marchini J. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–9. https://doi.org/10.1038/s41586-018-0579-z .
doi: 10.1038/s41586-018-0579-z
pubmed: 30305743
pmcid: 6786975
Collister JA, Liu X, Clifton L. Calculating polygenic risk scores (PRS) in UK Biobank: a practical guide for epidemiologists. Front Genet. 2022;13. https://doi.org/10.3389/fgene.2022.818574 .
Choi SW, O’Reilly PF. PRSice-2: polygenic risk score software for biobank-scale data. GigaScience. 2019;8(7):giz082. https://doi.org/10.1093/gigascience/giz082 .
doi: 10.1093/gigascience/giz082
pubmed: 31307061
pmcid: 6629542
Euesden J, Lewis CM, O’Reilly PF. PRSice: polygenic risk score software. Bioinformatics. 2015;31(9):1466–8. https://doi.org/10.1093/bioinformatics/btu848 .
doi: 10.1093/bioinformatics/btu848
pubmed: 25550326
Wray NR, Lee SH, Mehta D, Vinkhuyzen AAE, Dudbridge F, Middeldorp CM. Research Review: polygenic methods and their application to psychiatric traits. J Child Psychol Psychiatry. 2014;55(10):1068–87. https://doi.org/10.1111/jcpp.12295 .
doi: 10.1111/jcpp.12295
pubmed: 25132410
Alfaro-Almagro F, McCarthy P, Afyouni S, Andersson JLR, Bastiani M, Miller KL, Nichols TE, Smith SM. Confound modelling in UK Biobank brain imaging. NeuroImage. 2021;224:117002. https://doi.org/10.1016/j.neuroimage.2020.117002 .
doi: 10.1016/j.neuroimage.2020.117002
pubmed: 32502668
Smith SM, Nichols TE. Statistical challenges in Big Data Human Neuroimaging. Neuron. 2018;97(2):263–8. https://doi.org/10.1016/j.neuron.2017.12.018 .
doi: 10.1016/j.neuron.2017.12.018
pubmed: 29346749
Benjamini Y, Hochberg Y. Controlling the false Discovery rate: a practical and powerful Approach to multiple testing. J Roy Stat Soc: Ser B (Methodol). 1995;57(1):289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x .
doi: 10.1111/j.2517-6161.1995.tb02031.x
Kim H-J, Cho M-H, Shim WH, Kim JK, Jeon E-Y, Kim D-H, Yoon S-Y. Deficient autophagy in microglia impairs synaptic pruning and causes social behavioral defects. Mol Psychiatry. 2017;22(11):1576–84. https://doi.org/10.1038/mp.2016.103 .
doi: 10.1038/mp.2016.103
pubmed: 27400854
Ziats MN, Edmonson C, Rennert OM. (2015). The autistic brain in the context of normal neurodevelopment. Front Neuroanat. 9. https://doi.org/10.3389/fnana.2015.00115 .
Hallahan B, Daly EM, McAlonan G, Loth E, Toal F, O’Brien F, Robertson D, Hales S, Murphy C, Murphy KC, Murphy DGM. Brain morphometry volume in autistic spectrum disorder: a magnetic resonance imaging study of adults. Psychol Med. 2009;39(2):337–46. https://doi.org/10.1017/S0033291708003383 .
doi: 10.1017/S0033291708003383
pubmed: 18775096
Katuwal GJ, Baum SA, Cahill ND, Dougherty CC, Evans E, Evans DW, Moore GJ, Michael AM. Inter-method discrepancies in brain volume estimation may drive inconsistent findings in Autism. Front NeuroSci. 2016;10. https://doi.org/10.3389/fnins.2016.00439 .
Shen MD. Cerebrospinal fluid and the early brain development of autism. J Neurodevelopmental Disorders. 2018;10(1):39. https://doi.org/10.1186/s11689-018-9256-7 .
doi: 10.1186/s11689-018-9256-7
McKinney WS, Kelly SE, Unruh KE, Shafer RL, Sweeney JA, Styner M, Mosconi MW. Cerebellar volumes and Sensorimotor Behavior in Autism Spectrum Disorder. Front Integr Nuerosci. 2022;16. https://doi.org/10.3389/fnint.2022.821109 .
Wegiel J, Kuchna I, Nowicki K, Imaki H, Wegiel J, Marchi E, Ma SY, Chauhan A, Chauhan V, Bobrowicz TW, de Leon M, Louis LAS, Cohen IL, London E, Brown WT, Wisniewski T. The neuropathology of autism: defects of neurogenesis and neuronal migration, and dysplastic changes. Acta Neuropathol. 2010;119(6):755–70. https://doi.org/10.1007/s00401-010-0655-4 .
doi: 10.1007/s00401-010-0655-4
pubmed: 20198484
pmcid: 2869041
Skefos J, Cummings C, Enzer K, Holiday J, Weed K, Levy E, Yuce T, Kemper T, Bauman M. Regional alterations in Purkinje Cell Density in patients with autism. PLoS ONE. 2014;9(2):e81255. https://doi.org/10.1371/journal.pone.0081255 .
doi: 10.1371/journal.pone.0081255
pubmed: 24586223
pmcid: 3933333
DeRamus TP, Kana RK. Anatomical likelihood estimation meta-analysis of grey and white matter anomalies in autism spectrum disorders. NeuroImage: Clin. 2015;7:525–36. https://doi.org/10.1016/j.nicl.2014.11.004 .
doi: 10.1016/j.nicl.2014.11.004
pubmed: 25844306
Bolduc M-E, du Plessis AJ, Sullivan N, Guizard N, Zhang X, Robertson RL, Limperopoulos C. Regional cerebellar volumes predict functional outcome in children with cerebellar malformations. Cerebellum. 2012;11(2):531–42. https://doi.org/10.1007/s12311-011-0312-z .
doi: 10.1007/s12311-011-0312-z
pubmed: 21901523
Postema MC, van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Filho GB, Calderoni S, Calvo R, Daly E, Deruelle C, Di Martino A, Dinstein I, Duran FLS, Durston S, Ecker C, Ehrlich S, Fair D, Fedor J, … Francks C. Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets. Nat Commun 2019;10(1):4958. https://doi.org/10.1038/s41467-019-13005-8.
Olson IR, Hoffman LJ, Jobson KR, Popal HS, Wang Y. Little brain, little minds: the big role of the cerebellum in social development. Dev Cogn Neurosci. 2023;60:101238. https://doi.org/10.1016/j.dcn.2023.101238 .
doi: 10.1016/j.dcn.2023.101238
pubmed: 37004475
pmcid: 10067769
D’Mello AM, Stoodley CJ. (2015). Cerebro-cerebellar circuits in autism spectrum disorder. Front Neurosci. 9. https://doi.org/10.3389/fnins.2015.00408 .
Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, Collins R, Allen NE. Comparison of Sociodemographic and Health-related characteristics of UK Biobank participants with those of the General Population. Am J Epidemiol. 2017;186(9):1026–34. https://doi.org/10.1093/aje/kwx246 .
doi: 10.1093/aje/kwx246
pubmed: 28641372
pmcid: 5860371