#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Executive task-based brain function in children with type 1 diabetes: An observational study


Autoři: Lara C. Foland-Ross aff001;  Bruce Buckingam aff002;  Nelly Mauras aff003;  Ana Maria Arbelaez aff004;  William V. Tamborlane aff005;  Eva Tsalikian aff006;  Allison Cato aff007;  Gabby Tong aff001;  Kimberly Englert aff003;  Paul K. Mazaika aff001;  Allan L. Reiss aff001
Působiště autorů: Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, United States of America aff001;  Division of Pediatric Endocrinology and Diabetes, Stanford University School of Medicine, Stanford, California, United States of America aff002;  Division of Endocrinology, Diabetes and Metabolism, Nemours Children’s Health System, Jacksonville, Florida, United States of America aff003;  Division of Endocrinology, Washington University, Saint Louis, Missouri, United States of America aff004;  Division of Endocrinology, Yale University, New Haven, Connecticut, United States of America aff005;  Division of Endocrinology, University of Iowa, Iowa City, Iowa, United States of America aff006;  Division of Neurology, Nemours Children’s Health System, Jacksonville, Florida, United States of America aff007;  Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America aff008;  Department of Radiology, Stanford University School of Medicine, Stanford, California, United States of America aff009
Vyšlo v časopise: Executive task-based brain function in children with type 1 diabetes: An observational study. PLoS Med 16(12): e32767. doi:10.1371/journal.pmed.1002979
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pmed.1002979

Souhrn

Background

Optimal glycemic control is particularly difficult to achieve in children and adolescents with type 1 diabetes (T1D), yet the influence of dysglycemia on the developing brain remains poorly understood.

Methods and findings

Using a large multi-site study framework, we investigated activation patterns using functional magnetic resonance imaging (fMRI) in 93 children with T1D (mean age 11.5 ± 1.8 years; 45.2% female) and 57 non-diabetic (control) children (mean age 11.8 ± 1.5 years; 50.9% female) as they performed an executive function paradigm, the go/no-go task. Children underwent scanning and cognitive and clinical assessment at 1 of 5 different sites. Group differences in activation occurring during the contrast of “no-go > go” were examined while controlling for age, sex, and scan site. Results indicated that, despite equivalent task performance between the 2 groups, children with T1D exhibited increased activation in executive control regions (e.g., dorsolateral prefrontal and supramarginal gyri; p = 0.010) and reduced suppression of activation in the posterior node of the default mode network (DMN; p = 0.006). Secondary analyses indicated associations between activation patterns and behavior and clinical disease course. Greater hyperactivation in executive control regions in the T1D group was correlated with improved task performance (as indexed by shorter response times to correct “go” trials; r = −0.36, 95% CI −0.53 to −0.16, p < 0.001) and with better parent-reported measures of executive functioning (r values < −0.29, 95% CIs −0.47 to −0.08, p-values < 0.007). Increased deficits in deactivation of the posterior DMN in the T1D group were correlated with an earlier age of T1D onset (r = −0.22, 95% CI −0.41 to −0.02, p = 0.033). Finally, exploratory analyses indicated that among children with T1D (but not control children), more severe impairments in deactivation of the DMN were associated with greater increases in hyperactivation of executive control regions (T1D: r = 0.284, 95% CI 0.08 to 0.46, p = 0.006; control: r = 0.108, 95% CI −0.16 to 0.36, p = 0.423). A limitation to this study involves glycemic effects on brain function; because blood glucose was not clamped prior to or during scanning, future studies are needed to assess the influence of acute versus chronic dysglycemia on our reported findings. In addition, the mechanisms underlying T1D-associated alterations in activation are unknown.

Conclusions

These data indicate that increased recruitment of executive control areas in pediatric T1D may act to offset diabetes-related impairments in the DMN, ultimately facilitating cognitive and behavioral performance levels that are equivalent to that of non-diabetic controls. Future studies that examine whether these patterns change as a function of improved glycemic control are warranted.

Klíčová slova:

Cognitive impairment – Behavior – Functional magnetic resonance imaging – Neuroimaging – Glucose – Attention – Blood sugar


Zdroje

1. Traisman HS. Management of juvenile diabetes mellitus.St. Louis: Mosby; 1980.

2. International Diabetes Federation. IDF diabetes atlas. Brussels: International Diabetes Federation; 2013.

3. Giedd JN, Clasen LS, Lenroot R, Greenstein D, Wallace GL, Ordaz S, et al. Puberty-related influences on brain development. Mol Cell Endocrinol. 2006;254:154–62. doi: 10.1016/j.mce.2006.04.016 16765510

4. Luna B, Sweeney JA. Studies of brain and cognitive maturation through childhood and adolescence: a strategy for testing neurodevelopmental hypotheses. Schizophr Bull. 2001;27(3):443. doi: 10.1093/oxfordjournals.schbul.a006886 11596846

5. Siller AF, Lugar H, Rutlin J, Koller JM, Semenkovich K, White NH, et al. Severity of clinical presentation in youth with type 1 diabetes is associated with differences in brain structure. Pediatr Diabetes. 2017;18(8):686–95. doi: 10.1111/pedi.12420 27488913

6. Perantie DC, Lim A, Wu J, Weaver P, Warren SL, Sadler M, et al. Effects of prior hypoglycemia and hyperglycemia on cognition in children with type 1 diabetes mellitus. Pediatr Diabetes. 2008;9(2):87–95. doi: 10.1111/j.1399-5448.2007.00274.x 18208449

7. Aye T, Barnea-Goraly N, Ambler C, Hoang S, Schleifer K, Park Y, et al. White matter structural differences in young children with type 1 diabetes: a diffusion tensor imaging study. Diabetes Care. 2012;35(11):2167–73. doi: 10.2337/dc12-0017 22966090

8. Barnea-Goraly N, Raman M, Mazaika P, Marzelli M, Hershey T, Weinzimer SA, et al. Alterations in white matter structure in young children with type 1 diabetes. Diabetes Care. 2014;37(2):332–40. doi: 10.2337/dc13-1388 24319123

9. Marzelli MJ, Mazaika PK, Barnea-Goraly N, Hershey T, Tsalikian E, Tamborlane W, et al. Neuroanatomical correlates of dysglycemia in young children with type 1 diabetes. Diabetes. 2014;63(1):343–53. doi: 10.2337/db13-0179 24170697

10. Mauras N, Mazaika P, Buckingham B, Weinzimer S, White NH, Tsalikian E, et al. Longitudinal assessment of neuroanatomical and cognitive differences in young children with type 1 diabetes: association with hyperglycemia. Diabetes. 2014;64(5):1770–9. doi: 10.2337/db14-1445 25488901

11. Mazaika PK, Weinzimer SA, Mauras N, Buckingham B, White NH, Tsalikian E, et al. Variations in brain volume and growth in young children with type 1 diabetes. Diabetes. 2016;65(2):476–85. doi: 10.2337/db15-1242 26512024

12. Foland-Ross LC, Reiss AL, Mazaika PK, Mauras N, Weinzimer SA, Aye T, et al. Longitudinal assessment of hippocampus structure in children with type 1 diabetes. Pediatr Diabetes. 2018;19(6):1116–23.

13. Hosseini SH, Mazaika P, Mauras N, Buckingham B, Weinzimer SA, Tsalikian E, et al. Altered integration of structural covariance networks in young children with type 1 diabetes. Hum Brain Mapp. 2016;37(11):4034–46. doi: 10.1002/hbm.23293 27339089

14. Nunley KA, Rosano C, Ryan CM, Jennings JR, Aizenstein HJ, Zgibor JC, et al. Clinically relevant cognitive impairment in middle-aged adults with childhood-onset type 1 diabetes. Diabetes Care. 2015;38(9):1768–76. doi: 10.2337/dc15-0041 26153270

15. Ferguson SC, Blane A, Wardlaw J, Frier BM, Perros P, McCrimmon RJ, et al. Influence of an early-onset age of type 1 diabetes on cerebral structure and cognitive function. Diabetes Care. 2005;28(6):1431–7. doi: 10.2337/diacare.28.6.1431 15920064

16. Gaudieri PA, Chen R, Greer TF, Holmes CS. Cognitive function in children with type 1 diabetes a meta-analysis. Diabetes Care. 2008;31(9):1892–7. doi: 10.2337/dc07-2132 18753668

17. Naguib JM, Kulinskaya E, Lomax CL, Garralda ME. Neuro-cognitive performance in children with type 1 diabetes—a meta-analysis. J Pediatr Psychol. 2008;34(3):271–82. doi: 10.1093/jpepsy/jsn074 18635605

18. Broadley MM, White MJ, Andrew B. A systematic review and meta-analysis of executive function performance in type 1 diabetes mellitus. Psychosom Med. 2017;79(6):684–96. doi: 10.1097/PSY.0000000000000460 28207612

19. Gallardo-Moreno GB, González-Garrido AA, Gudayol-Ferré E, Guàrdia-Olmos J. Type 1 diabetes modifies brain activation in young patients while performing visuospatial working memory tasks. J Diabetes Res. 2015;2015:703512. doi: 10.1155/2015/703512 26266268

20. Bolo NR, Musen G, Jacobson AM, Weinger K, McCartney RL, Flores V, et al. Brain activation during working memory is altered in type 1 diabetes during hypoglycemia. Diabetes. 2011;60(12):3256–64. doi: 10.2337/db11-0506 21984582

21. Guàrdia-Olmos J, Gallardo-Moreno GB, Gudayol-Ferré E, Peró-Cebollero M, González-Garrido AA. Effect of verbal task complexity in a working memory paradigm in patients with type 1 diabetes. A fMRI study. PLoS ONE. 2017;12(6):e0178172. doi: 10.1371/journal.pone.0178172 28582399

22. Van Duinkerken E, Schoonheim MM, Sanz-Arigita EJ, IJzerman RG, Moll AC, Snoek FJ, et al. Resting-state brain networks in type 1 diabetic patients with and without microangiopathy and their relation to cognitive functions and disease variables. Diabetes. 2012;61(7):1814–21. doi: 10.2337/db11-1358 22438575

23. Bolo NR, Musen G, Simonson DC, Nickerson LD, Flores VL, Siracusa T, et al. Functional connectivity of insula, basal ganglia, and prefrontal executive control networks during hypoglycemia in type 1 diabetes. J Neurosci. 2015;35(31):11012–23. doi: 10.1523/JNEUROSCI.0319-15.2015 26245963

24. Saggar M, Tsalikian E, Mauras N, Mazaika P, White NH, Weinzimer S, et al. Compensatory hyper-connectivity in developing brains of young children with type 1 diabetes. Diabetes. 2016;66(3):754–62. doi: 10.2337/db16-0414 27702833

25. Luce RD. Response times: their role in inferring elementary mental organization. Oxford: Oxford University Press; 1986.

26. Donders FC. On the speed of mental processes. Acta Psychol (Amst). 1969;30:412–31.

27. Reynolds CR, Kamphaus RW. Behavior assessment system for children. 2nd edition. Circle Pines (MN): American Guidance Service; 2004.

28. Gioia GA, Isquith PK, Guy SC, Kenworthy L. Behavior rating inventory of executive functions. Lutz (FL): Psychological Assessment Resources; 2000.

29. Wechsler D. Wechsler intelligence scale for children, fourth edition (WISC-IV). San Antonio (TX): Psychological Corporation; 2003.

30. Jack CR, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et al. The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685–91. doi: 10.1002/jmri.21049 18302232

31. Smith SM. Fast robust automated brain extraction. Hum Brain Mapp. 2002;17(3):143–55. doi: 10.1002/hbm.10062 12391568

32. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002;17(2):825–41. doi: 10.1016/s1053-8119(02)91132-8 12377157

33. Woolrich MW, Ripley BD, Brady M, Smith SM. Temporal autocorrelation in univariate linear modeling of FMRI data. Neuroimage. 2001;14(6):1370–86. doi: 10.1006/nimg.2001.0931 11707093

34. Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med Image Anal. 2001;5(2):143–56. doi: 10.1016/s1361-8415(01)00036-6 11516708

35. Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL, et al. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage. 2011;54(1):313–27. doi: 10.1016/j.neuroimage.2010.07.033 20656036

36. Fonov VS, Evans AC, McKinstry RC, Almli C, Collins D. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. Neuroimage. 2009;47:S102.

37. Woolrich M. Robust group analysis using outlier inference. Neuroimage. 2008;41(2):286–301. doi: 10.1016/j.neuroimage.2008.02.042 18407525

38. Worsley KJ. Statistical analysis of activation images. In: Jezzard P, Matthews PM, Smith SM, editors. Functional MRI: an introduction to methods. New York: Oxford University Press; 2001.

39. Hochberg Y. A sharper Bonferroni procedure for multiple tests of significance. Biometrika. 1988;75(4):800–2.

40. Raichle ME. The brain’s default mode network. Annu Rev Neurosci. 2015;38:433–47. doi: 10.1146/annurev-neuro-071013-014030 25938726

41. Anticevic A, Repovs G, Shulman GL, Barch DM. When less is more: TPJ and default network deactivation during encoding predicts working memory performance. Neuroimage. 2010;49(3):2638–48. doi: 10.1016/j.neuroimage.2009.11.008 19913622

42. Daselaar S, Prince S, Cabeza R. When less means more: deactivations during encoding that predict subsequent memory. Neuroimage. 2004;23(3):921–7. doi: 10.1016/j.neuroimage.2004.07.031 15528092

43. Anticevic A, Gancsos M, Murray JD, Repovs G, Driesen NR, Ennis DJ, et al. NMDA receptor function in large-scale anticorrelated neural systems with implications for cognition and schizophrenia. Proc Natl Acad Sci U S A. 2012;109(41):16720–5. doi: 10.1073/pnas.1208494109 23012427

44. Hayden BY, Smith DV, Platt ML. Electrophysiological correlates of default-mode processing in macaque posterior cingulate cortex. Proc Natl Acad Sci U S A. 2009;106(14):5948–53. doi: 10.1073/pnas.0812035106 19293382

45. Buckner R, Andrews-Hanna J, Schacter D. The brain’s default network: anatomy, function, and relevance to disease. Ann NY Acad Sci. 2008;1124:1–38. doi: 10.1196/annals.1440.011 18400922

46. Anticevic A, Cole MW, Murray JD, Corlett PR, Wang X-J, Krystal JH. The role of default network deactivation in cognition and disease. Trends Cogn Sci. 2012;16(12):584–92. doi: 10.1016/j.tics.2012.10.008 23142417

47. Grady CL, Protzner AB, Kovacevic N, Strother SC, Afshin-Pour B, Wojtowicz M, et al. A multivariate analysis of age-related differences in default mode and task-positive networks across multiple cognitive domains. Cereb Cortex. 2009;20(6):1432–47. doi: 10.1093/cercor/bhp207 19789183

48. Lustig C, Snyder AZ, Bhakta M, O’Brien KC, McAvoy M, Raichle ME, et al. Functional deactivations: change with age and dementia of the Alzheimer type. Proc Natl Acad Sci U S A. 2003;100(24):14504–9. doi: 10.1073/pnas.2235925100 14608034

49. Dickerson BC, Salat DH, Bates JF, Atiya M, Killiany RJ, Greve DN, et al. Medial temporal lobe function and structure in mild cognitive impairment. Ann Neurol. 2004;56(1):27–35. doi: 10.1002/ana.20163 15236399

50. Pihlajamäki M, Sperling RA. Functional MRI assessment of task-induced deactivation of the default mode network in Alzheimer’s disease and at-risk older individuals. Behav Neurol. 2009;21(1–2):77–91.

Štítky
Interné lekárstvo

Článok vyšiel v časopise

PLOS Medicine


2019 Číslo 12
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Aktuální možnosti diagnostiky a léčby litiáz
nový kurz
Autori: MUDr. Tomáš Ürge, PhD.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#