Characterizing the Subcortical Structures in Youth with Congenital Heart Disease

References

1. 1.↵
   1. Marino BS,
   2. Lipkin PH,
   3. Newburger JW, et al

   ; American Heart Association Congenital Heart Defects Committee, Council on Cardiovascular Disease in the Young, Council on Cardiovascular Nursing, and Stroke Council. Neurodevelopmental outcomes in children with congenital heart disease: evaluation and management: a scientific statement from the American Heart Association. Circulation 2012;126:1143–72 doi:10.1161/CIR.0b013e318265ee8a pmid:22851541

   Abstract/FREE Full Text
2. 2.↵
   1. Easson K,
   2. Dahan-Oliel N,
   3. Rohlicek C, et al

   . A comparison of developmental outcomes of adolescent neonatal intensive care unit survivors born with a congenital heart defect or born preterm. J Pediatr 2019;207: 34–41.e2 doi:10.1016/j.jpeds.2018.11.002 pmid:30528759

   CrossRefPubMed
3. 3.↵
   1. Latal B

   . Neurodevelopmental outcomes of the child with congenital heart disease. Clin Perinatol 2016;43:173–85 doi:10.1016/j.clp.2015.11.012 pmid:26876129

   CrossRefPubMed
4. 4.↵
   1. Keir M,
   2. Ebert P,
   3. Kovacs AH, et al

   . Neurocognition in adult congenital heart disease: how to monitor and prevent progressive decline. Can J Cardiol 2019;35:1675–85 doi:10.1016/j.cjca.2019.06.020 pmid:31570238

   CrossRefPubMed
5. 5.↵
   1. Bolduc ME,
   2. Lambert H,
   3. Ganeshamoorthy S, et al

   . Structural brain abnormalities in adolescents and young adults with congenital heart defect: a systematic review. Dev Med Child Neurol 2018;60:1209–24 doi:10.1111/dmcn.13975 pmid:30028505

   CrossRefPubMed
6. 6.↵
   1. Voineskos AN,
   2. Winterburn JL,
   3. Felsky D, et al

   . Hippocampal (subfield) volume and shape in relation to cognitive performance across the adult lifespan. Hum Brain Mapp 2015;36:3020–37 doi:10.1002/hbm.22825 pmid:25959503

   CrossRefPubMed
7. 7.↵
   1. Chakravarty MM,
   2. Rapoport JL,
   3. Giedd JN, et al

   . Striatal shape abnormalities as novel neurodevelopmental endophenotypes in schizophrenia: a longitudinal study. Hum Brain Mapp 2015;36:1458–69 doi:10.1002/hbm.22715 pmid:25504933

   CrossRefPubMed
8. 8.↵
   1. Schuetze M,
   2. Park MT,
   3. Cho IY, et al

   . Morphological alterations in the thalamus, striatum, and pallidum in autism spectrum disorder. Neuropsychopharmacology 2016;41:2627–37 doi:10.1038/npp.2016.64 pmid:27125303

   CrossRefPubMed
9. 9.↵
   1. Watson CG,
   2. Asaro LA,
   3. Wypij D, et al

   . Altered gray matter in adolescents with d-transposition of the great arteries. J Pediatr 2016;169: 36–43 e1 doi:10.1016/j.jpeds.2015.09.084 pmid:26553098

   CrossRefPubMed
10. 10.↵
    1. von Rhein M,
    2. Buchmann A,
    3. Hagmann C, et al

    . Brain volumes predict neurodevelopment in adolescents after surgery for congenital heart disease. Brain 2014;137:268–76 doi:10.1093/brain/awt322 pmid:24277720

    CrossRefPubMedWeb of Science
11. 11.↵
    1. Singh S,
    2. Kumar R,
    3. Roy B, et al

    . Regional brain gray matter changes in adolescents with single ventricle heart disease. Neurosci Lett 2018;665:156–62 doi:10.1016/j.neulet.2017.12.011 pmid:29222023

    CrossRefPubMed
12. 12.↵
    1. Fontes K,
    2. Rohlicek CV,
    3. Saint‐Martin C, et al

    . Hippocampal alterations and functional correlates in adolescents and young adults with congenital heart disease. Hum Brain Mapp 2019;40:3548–60 doi:10.1002/hbm.24615 pmid:31070841

    CrossRefPubMed
13. 13.↵
    1. Delgado MR,
    2. Li J,
    3. Schiller D, et al

    . The role of the striatum in aversive learning and aversive prediction errors. Philos Trans R Soc Lond, B, Biol Sci 2008;363:3787–800 doi:10.1098/rstb.2008.0161 pmid:18829426

    CrossRefPubMed
14. 14.↵
    1. Gambaryan LS,
    2. Sarkisyan ZS

    . Role of the globus pallidus in the mechanisms of memory. Neurosci Behav Physiol Neuroscience Physiol 1983;13:470–75 doi:10.1007/BF01182692 pmid:6672661

    CrossRefPubMed
15. 15.↵
    1. van Groen T,
    2. Kadish I,
    3. Wyss JM

    . The role of the laterodorsal nucleus of the thalamus in spatial learning and memory in the rat. Behav Brain Res 2002;136:329–37 doi:10.1016/S0166-4328(02)00199-7 pmid:12429394

    CrossRefPubMedWeb of Science
16. 16.↵
    1. Hollingshead AB

    . Four Factor Index of Social Status [1975]. https://ubir.buffalo.edu/xmlui/handle/10477/1879. Accessed September 15, 2019
17. 17.↵
    1. Bedford SA,
    2. Park MT,
    3. Devenyi GA, et al

    ; MRC AIMS Consortium. Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder. Mol Psychiatry 2020;25:614–28 doi:10.1038/s41380-019-0420-6 pmid:31028290

    CrossRefPubMed
18. 18.↵
    1. Sadedin SP,
    2. Pope B,
    3. Oshlack A

    . Bpipe: a tool for running and managing bioinformatics pipelines. Bioinformatics 2012;28:1525–26 doi:10.1093/bioinformatics/bts167 pmid:22500002

    CrossRefPubMedWeb of Science
19. 19.↵
    1. Tustison NJ,
    2. Avants BB,
    3. Cook PA, et al

    . N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 2010;29:1310–20 doi:10.1109/TMI.2010.2046908 pmid:20378467

    CrossRefPubMedWeb of Science
20. 20.↵
    1. Eskildsen SF,
    2. Coupé P,
    3. Fonov V, et al

    ; Alzheimer's Disease Neuroimaging Initiative. BEaST: brain extraction based on nonlocal segmentation technique. Neuroimage 2012;59:2362–73 doi:10.1016/j.neuroimage.2011.09.012 pmid:21945694

    CrossRefPubMed
21. 21.↵
    1. Chakravarty MM,
    2. Steadman P,
    3. van Eede MC, et al

    . Performing label-fusion-based segmentation using multiple automatically generated templates. Hum Brain Mapp 2013;34:2635–54 doi:10.1002/hbm.22092 pmid:22611030

    CrossRefPubMedWeb of Science
22. 22.↵
    1. Raznahan A,
    2. Shaw PW,
    3. Lerch JP, et al

    . Longitudinal four-dimensional mapping of subcortical anatomy in human development. Proc Natl Acad Sci USA 2014;111:1592–97 doi:10.1073/pnas.1316911111 pmid:24474784

    Abstract/FREE Full Text
23. 23.↵
    1. Shaw P,
    2. De Rossi P,
    3. Watson B, et al

    . Mapping the development of the basal ganglia in children with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 2014;53:780–89.e11 doi:10.1016/j.jaac.2014.05.003 pmid:24954827

    CrossRefPubMed
24. 24.↵
    1. Tullo S,
    2. Devenyi GA,
    3. Patel R, et al

    . Warping an atlas derived from serial histology to 5 high-resolution MRIs. Sci Data 2018;5:180107 doi:10.1038/sdata.2018.107 pmid:29917012

    CrossRefPubMed
25. 25.↵
    1. Makowski C,
    2. Béland S,
    3. Kostopoulos P, et al

    . Evaluating accuracy of striatal, pallidal, and thalamic segmentation methods: comparing automated approaches to manual delineation. Neuroimage 2018;170: 182–98 doi:10.1016/j.neuroimage.2017.02.069 pmid:28259781

    CrossRefPubMed
26. 26.↵
    1. Chris L,
    2. Gruner D, G
    3. roer L, et al

    . SciNet: lessons learned from building a power-efficient top-20 system and data centre. Journal of Physics: Conference Series 2010;256:012026
27. 27.↵
    1. Lerch J

    . Voxel-wise morphometry using RMINC. 2010. https://rdrr.io/github/Mouse-Imaging-Centre/RMINC/f/inst/documentation/RMINC-userGuide.pdf. Accessed September 15, 2019
28. 28.↵
    The R Core Team. A Language and Environment for Statistical Computing. 2013. https://cran.r-project.org/doc/manuals/fullrefman.pdf. Accessed September 15, 2019
29. 29.↵
    1. Kalin AM,
    2. Park MT,
    3. Chakravarty MM, et al

    . Subcortical shape changes, hippocampal atrophy and cortical thinning in future Alzheimer’s disease patients. Front Aging Neurosci 2017;9:38 doi:10.3389/fnagi.2017.00038 pmid:28326033

    CrossRefPubMed
30. 30.↵
    1. Keir M,
    2. Ebert P,
    3. Kovacs AH, et al

    . Neurocognition in adult congenital heart disease: how to monitor and prevent progressive decline. Can J Cardiol 2019;35:1675–85 doi:10.1016/j.cjca.2019.06.02]0 pmid:31570238

    CrossRefPubMed
31. 31.↵
    1. Garza-Villarreal EA,
    2. Chakravarty MM,
    3. Hansen B, et al

    . The effect of crack cocaine addiction and age on the microstructure and morphology of the human striatum and thalamus using shape analysis and fast diffusion kurtosis imaging. Transl Psychiatry 2017;7:e1122 doi:10.1038/tp.2017.92 pmid:28485734

    CrossRefPubMed
32. 32.↵
    1. Janes AC,
    2. Park MT,
    3. Farmer S, et al

    . Striatal morphology is associated with tobacco cigarette craving. Neuropsychopharmacology 2015;40:406–11 doi:10.1038/npp.2014.185 pmid:25056595

    CrossRefPubMed
33. 33.↵
    1. Smith MJ,
    2. Cobia DJ,
    3. Wang L, et al

    . Cannabis-related working memory deficits and associated subcortical morphological differences in healthy individuals and schizophrenia subjects. Schizophr Bull 2014;40:287–99 doi:10.1093/schbul/sbt176 pmid:24342821

    CrossRefPubMedWeb of Science
34. 34.↵
    1. Miller SP,
    2. McQuillen PS

    . Neurology of congenital heart disease: insight from brain imaging. Arch Dis Child Fetal Neonatal Ed 2007;92:F435–37 doi:10.1136/adc.2006.108845 pmid:17848505

    FREE Full Text
35. 35.↵
    1. Brossard-Racine M,
    2. du Plessis AJ,
    3. Vezina G, et al

    . Prevalence and spectrum of in utero structural brain abnormalities in fetuses with complex congenital heart disease. AJNR Am J Neuroradiol 2014;35:1593–99 doi:10.3174/ajnr.A3903 pmid:24651820

    Abstract/FREE Full Text