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Lower Working Memory Performance in Overweight and Obese Adolescents Is Mediated by White Matter Microstructure

Published online by Cambridge University Press:  28 December 2015

Gabriela Alarcón
Affiliation:
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon
Siddharth Ray
Affiliation:
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon
Bonnie J. Nagel*
Affiliation:
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon Department of Psychiatry, Oregon Health & Science University, Portland, Oregon
*
Correspondence and reprint requests to: Bonnie J. Nagel, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, DC7P, Portland, OR 97239. E-mail: [email protected]

Abstract

Objectives: Elevated body mass index (BMI) is associated with deficits in working memory, reduced gray matter volume in frontal and parietal lobes, as well as changes in white matter (WM) microstructure. The current study examined whether BMI was related to working memory performance and blood oxygen level dependent (BOLD) activity, as well as WM microstructure during adolescence. Methods: Linear regressions with BMI and (1) verbal working memory BOLD signal, (2) spatial working memory BOLD signal, and (3) fractional anisotropy (FA), a measure of WM microstructure, were conducted in a sample of 152 healthy adolescents ranging in BMI. Results: BMI was inversely related to IQ and verbal and spatial working memory accuracy; however, there was no significant relationship between BMI and BOLD response for either verbal or spatial working memory. Furthermore, BMI was negatively correlated with FA in the left superior longitudinal fasciculus (SLF) and left inferior longitudinal fasciculus (ILF). ILF FA and IQ significantly mediated the relationship between BMI and verbal working memory performance, whereas SLF FA, but not IQ, significantly mediated the relationship between BMI and accuracy of both verbal and spatial working memory. Conclusions: These findings indicate that higher BMI is associated with decreased FA in WM fibers connecting brain regions that support working memory, and that WM microstructural deficits may underlie inferior working memory performance in youth with higher BMI. Of interest, BMI did not show the same relationship with working memory BOLD activity, which may indicate that changes in brain structure precede changes in function. (JINS, 2015, 21, 281–292)

Type
Research Articles
Copyright
Copyright © The International Neuropsychological Society 2015 

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References

Alarcon, G., Cservenka, A., Fair, D.A., & Nagel, B.J. (2014). Sex differences in the neural substrates of spatial working memory during adolescence are not mediated by endogenous testosterone. Brain Research, 1593, 4054. doi:10.1016/j.brainres.2014.09.057 CrossRefGoogle Scholar
Alosco, M.L., Stanek, K.M., Galioto, R., Korgaonkar, M.S., Grieve, S.M., Brickman, A.M., & Gunstad, J. (2014). Body mass index and brain structure in healthy children and adolescents. The International Journal of Neuroscience, 124(1), 4955. doi:10.3109/00207454.2013.817408 CrossRefGoogle ScholarPubMed
Bastard, J.P., Maachi, M., Lagathu, C., Kim, M.J., Caron, M., Vidal, H., & Feve, B. (2006). Recent advances in the relationship between obesity, inflammation, and insulin resistance. European Cytokine Network, 17(1), 412. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/16613757 Google Scholar
Bray, G.A., & Gallagher, T.F. Jr (1975). Manifestations of hypothalamic obesity in man: A comprehensive investigation of eight patients and a reveiw of the literature. Medicine, 54(4), 301330. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/1152672 Google Scholar
Chiang, M.C., Barysheva, M., Shattuck, D.W., Lee, A.D., Madsen, S.K., Avedissian, C., & Thompson, P.M. (2009). Genetics of brain fiber architecture and intellectual performance. The Journal of Neuroscience, 29(7), 22122224. doi:10.1523/JNEUROSCI.4184-08.2009 Google Scholar
Choi, J., Jeong, B., Polcari, A., Rohan, M.L., & Teicher, M.H. (2012). Reduced fractional anisotropy in the visual limbic pathway of young adults witnessing domestic violence in childhood. Neuroimage, 59(2), 10711079. doi:10.1016/j.neuroimage.2011.09.033 Google Scholar
Cohen, M.S. (1997). Parametric analysis of fMRI data using linear systems methods. Neuroimage, 6(2), 93103. doi:10.1006/nimg.1997.0278 Google Scholar
Cox, R.W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162173. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/8812068 Google Scholar
Cservenka, A., Herting, M.M., & Nagel, B.J. (2012). Atypical frontal lobe activity during verbal working memory in youth with a family history of alcoholism. Drug and Alcohol Dependence, 123(1–3), 98104. doi:10.1016/j.drugalcdep.2011.10.021 Google Scholar
Darki, F., & Klingberg, T. (2015). The role of fronto-parietal and fronto-striatal networks in the development of working memory: A longitudinal study. Cerebral Cortex, 25, 15871595. doi:10.1093/cercor/bht352 Google Scholar
Doyle, P., Cusin, I., Rohner-Jeanrenaud, F., & Jeanrenaud, B. (1995). Four-day hyperinsulinemia in euglycemic conditions alters local cerebral glucose utilization in specific brain nuclei of freely moving rats. Brain Research, 684(1), 4755. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/7583203 Google Scholar
Forman, S.D., Cohen, J.D., Fitzgerald, M., Eddy, W.F., Mintun, M.A., & Noll, D.C. (1995). Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): Use of a cluster-size threshold. Magnetic Resonance in Medicine, 33(5), 636647. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/7596267 CrossRefGoogle ScholarPubMed
Gazdzinski, S., Kornak, J., Weiner, M.W., & Meyerhoff, D.J. (2008). Body mass index and magnetic resonance markers of brain integrity in adults. Annals of Neurology, 63(5), 652657. doi:10.1002/ana.21377 Google Scholar
Gogtay, N., Giedd, J.N., Lusk, L., Hayashi, K.M., Greenstein, D., Vaituzis, A.C., & Thompson, P.M. (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences of the United States of America, 101(21), 81748179. doi:10.1073/pnas.0402680101 Google Scholar
Gonzales, M.M., Tarumi, T., Miles, S.C., Tanaka, H., Shah, F., & Haley, A.P. (2010). Insulin sensitivity as a mediator of the relationship between BMI and working memory-related brain activation. Obesity (Silver Spring), 18(11), 21312137.CrossRefGoogle ScholarPubMed
Hayes, A.F. (2013). Introduction to mediation, moderation, and conditional process analysis. New York, NY: The Guilford Press.Google Scholar
Herting, M.M., Maxwell, E.C., Irvine, C., & Nagel, B.J. (2012). The impact of sex, puberty, and hormones on white matter microstructure in adolescents. Cerebral Cortex, 22(9), 19791992. doi:10.1093/cercor/bhr246 CrossRefGoogle ScholarPubMed
Herting, M.M., Schwartz, D., Mitchell, S.H., & Nagel, B.J. (2010). Delay discounting behavior and white matter microstructure abnormalities in youth with a family history of alcoholism. Alcoholism, Clinical and Experimental Research, 34(9), 15901602. doi:10.1111/j.1530-0277.2010.01244.x Google Scholar
Hollingshead, A.A. (1975). Four-factor index of social status. New Haven, CT: Yale University.Google Scholar
Jayaraman, A., Lent-Schochet, D., & Pike, C.J. (2014). Diet-induced obesity and low testosterone increase neuroinflammation and impair neural function. Journal of Neuroinflammation, 11, 162. doi:10.1186/s12974-014-0162-y Google Scholar
Jenkinson, M. (2003). Fast, automated, N-dimensional phase-unwrapping algorithm. Magnetic Resonance in Medicine, 49(1), 193197. doi:10.1002/mrm.10354 Google Scholar
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17(2), 825841. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12377157 Google Scholar
King, B.M. (2006). Amygdaloid lesion-induced obesity: Relation to sexual behavior, olfaction, and the ventromedial hypothalamus. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 291(5), R1201R1214. doi:10.1152/ajpregu.00199.2006 Google Scholar
Klingberg, T., Forssberg, H., & Westerberg, H. (2002). Increased brain activity in frontal and parietal cortex underlies the development of visuospatial working memory capacity during childhood. Journal of Cognitive Neuroscience, 14(1), 110. doi:10.1162/089892902317205276 Google Scholar
Krafft, C.E., Schaeffer, D.J., Schwarz, N.F., Chi, L., Weinberger, A.L., Pierce, J.E., & McDowell, J.E. (2014). Improved frontoparietal white matter integrity in overweight children is associated with attendance at an after-school exercise program. Developmental Neuroscience, 36(1), 19. doi:10.1159/000356219 Google Scholar
Kullmann, S., Schweizer, F., Veit, R., Fritsche, A., & Preissl, H. (2015). Compromised white matter integrity in obesity. Obesity Reviews, 16(4), 273281. doi:10.1111/obr.12248 CrossRefGoogle ScholarPubMed
Kwon, H., Reiss, A.L., & Menon, V. (2002). Neural basis of protracted developmental changes in visuo-spatial working memory. Proceedings of the National Academy of Sciences of the United States of America, 99(20), 1333613341. doi:10.1073/pnas.162486399 Google Scholar
Lebel, C., & Beaulieu, C. (2011). Longitudinal development of human brain wiring continues from childhood into adulthood. The Journal of Neuroscience, 31(30), 1093710947. doi:10.1523/JNEUROSCI.5302-10.2011 CrossRefGoogle ScholarPubMed
Lee, F.S., Heimer, H., Giedd, J.N., Lein, E.S., Sestan, N., Weinberger, D.R., & Casey, B.J. (2014). Mental health. Adolescent mental health--Opportunity and obligation. Science, 346(6209), 547549. doi:10.1126/science.1260497 Google Scholar
Mackiewicz Seghete, K.L., Cservenka, A., Herting, M.M., & Nagel, B.J. (2013). Atypical spatial working memory and task-general brain activity in adolescents with a family history of alcoholism. Alcoholism, Clinical and Experimental Research, 37(3), 390398. doi:10.1111/j.1530-0277.2012.01948.x Google Scholar
Martin, A., Saunders, D.H., Shenkin, S.D., & Sproule, J. (2014). Lifestyle intervention for improving school achievement in overweight or obese children and adolescents. The Cochrane Database of Systematic Reviews, 3, CD009728. doi:10.1002/14651858.CD009728.pub2 Google Scholar
Miller, A.A., & Spencer, S.J. (2014). Obesity and neuroinflammation: A pathway to cognitive impairment. Brain, Behavior, and Immunity, 42, 1021. doi:10.1016/j.bbi.2014.04.001 Google Scholar
Moses, P., DiNino, M., Hernandez, L., & Liu, T.T. (2014). Developmental changes in resting and functional cerebral blood flow and their relationship to the BOLD response. Human Brain Mapping, 35(7), 31883198. doi:10.1002/hbm.22394 Google Scholar
Moses, P., Hernandez, L.M., & Orient, E. (2014). Age-related differences in cerebral blood flow underlie the BOLD fMRI signal in childhood. Frontiers in Psychology, 5, 300. doi:10.3389/fpsyg.2014.00300 Google Scholar
Muniyappa, R., Iantorno, M., & Quon, M.J. (2008). An integrated view of insulin resistance and endothelial dysfunction. Endocrinology and Metabolism Clinics of North America, 37(3), 685711. ix-x doi:10.1016/j.ecl.2008.06.001 Google Scholar
Nagel, B.J., Herting, M.M., Maxwell, E.C., Bruno, R., & Fair, D. (2013). Hemispheric lateralization of verbal and spatial working memory during adolescence. Brain and Cognition, 82(1), 5868. doi:10.1016/j.bandc.2013.02.007 Google Scholar
Ogawa, S., Lee, T.M., Kay, A.R., & Tank, D.W. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences of the United States of America, 87(24), 98689872. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/2124706 CrossRefGoogle ScholarPubMed
Ogawa, S., Tank, D.W., Menon, R., Ellermann, J.M., Kim, S.G., Merkle, H., & Ugurbil, K. (1992). Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging. Proceedings of the National Academy of Sciences of the United States of America, 89(13), 59515955. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/1631079 CrossRefGoogle ScholarPubMed
Ogden, C.L., Carroll, M.D., Kit, B.K., & Flegal, K.M. (2014). Prevalence of childhood and adult obesity in the United States, 2011-2012. The Journal of the American Medical Association, 311(8), 806814. doi:10.1001/jama.2014.732 CrossRefGoogle ScholarPubMed
Oishi, K., Faria, A., van Zijl, P.C.M., & Mori, S. (2011). MRI atlas of human white matter (2nd ed.). London, UK: Academic Press.Google Scholar
Peters, B.D., Szeszko, P.R., Radua, J., Ikuta, T., Gruner, P., DeRosse, P., & Malhotra, A.K. (2012). White matter development in adolescence: Diffusion tensor imaging and meta-analytic results. Schizophrenia Bulletin, 38(6), 13081317. doi:10.1093/schbul/sbs054 CrossRefGoogle ScholarPubMed
Petersen, A.C., Crockett, L., Richards, M., & Boxer, A. (1988). A self-report measure of pubertal status: Reliability, validity, and initial norms. Journal of Youth and Adolescence, 17(2), 117133. doi:10.1007/BF01537962 Google Scholar
Purnell, J.Q., Lahna, D.L., Samuels, M.H., Rooney, W.D., & Hoffman, W.F. (2014). Loss of pons-to-hypothalamic white matter tracks in brainstem obesity. International Journal of Obesity, 38(12), 15731577. doi:10.1038/ijo.2014.57 Google Scholar
Reinert, K.R., Po’e, E.K., & Barkin, S.L. (2013). The relationship between executive function and obesity in children and adolescents: A systematic literature review. Journal of Obesity, 2013, 820956. doi:10.1155/2013/820956 Google Scholar
Schaeffer, D.J., Krafft, C.E., Schwarz, N.F., Chi, L., Rodrigue, A.L., Pierce, J.E., & McDowell, J.E. (2014). An 8-month exercise intervention alters frontotemporal white matter integrity in overweight children. Psychophysiology, 51(8), 728733. doi:10.1111/psyp.12227 Google Scholar
Seghete, K.L., Herting, M.M., & Nagel, B.J. (2013). White matter microstructure correlates of inhibition and task-switching in adolescents. Brain Research, 1527, 1528. doi:10.1016/j.brainres.2013.06.003 CrossRefGoogle ScholarPubMed
Sena, A., Sarlieve, L.L., & Rebel, G. (1985). Brain myelin of genetically obese mice. Journal of the Neurological Sciences, 68(2-3), 233243. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/2989440 Google Scholar
Shefer, G., Marcus, Y., & Stern, N. (2013). Is obesity a brain disease? Neuroscience and Biobehavioral Reviews, 37(10 Pt 2), 24892503. doi:10.1016/j.neubiorev.2013.07.015 Google Scholar
Smith, S.M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143155. doi:10.1002/hbm.10062 CrossRefGoogle ScholarPubMed
Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E.,& Behrens, T.E. (2006). Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. Neuroimage, 31(4), 14871505. doi:10.1016/j.neuroimage.2006.02.024 Google Scholar
Sun, S.W., Liang, H.F., Trinkaus, K., Cross, A.H., Armstrong, R.C., & Song, S.K. (2006). Noninvasive detection of cuprizone induced axonal damage and demyelination in the mouse corpus callosum. Magnetic Resonance in Medicine, 55(2), 302308. doi:10.1002/mrm.20774 Google Scholar
Van den Eynde, F., & Treasure, J. (2009). Neuroimaging in eating disorders and obesity: Implications for research. Child and Adolescent Psychiatric Clinics of North America, 18(1), 95115. doi:10.1016/j.chc.2008.07.016 Google Scholar
Volkow, N.D., Wang, G.J., Telang, F., Fowler, J.S., Goldstein, R.Z., Alia-Klein, N., & Pradhan, K. (2009). Inverse association between BMI and prefrontal metabolic activity in healthy adults. Obesity (Silver Spring), 17(1), 6065. doi:10.1038/oby.2008.469 Google Scholar
Weschler, D. (1999). Wechsler Abbreviated Scale of Intelligence (WASI). San Antonio, TX: Harcourt Assessment.Google Scholar
Whitaker, R.C., Wright, J.A., Pepe, M.S., Seidel, K.D., & Dietz, W.H. (1997). Predicting obesity in young adulthood from childhood and parental obesity. The New England Journal of Medicine, 337(13), 869873. doi:10.1056/NEJM199709253371301 CrossRefGoogle ScholarPubMed
Willeumier, K.C., Taylor, D.V., & Amen, D.G. (2011). Elevated BMI is associated with decreased blood flow in the prefrontal cortex using SPECT imaging in healthy adults. Obesity (Silver Spring), 19(5), 10951097. doi:10.1038/oby.2011.16 Google Scholar
Xiong, J., Gao, J.-H., Lancaster, J.L., & Fox, P.T. (1995). Clustered pixel analysis for functional MRI activation studies of the human brain. Human Brain Mapping, 3, 287301.Google Scholar
Yau, P.L., Castro, M.G., Tagani, A., Tsui, W.H., & Convit, A. (2012). Obesity and metabolic syndrome and functional and structural brain impairments in adolescence. Pediatrics, 130(4), e856e864. doi:10.1542/peds.2012-0324 Google Scholar
Yau, P.L., Kang, E.H., Javier, D.C., & Convit, A. (2014). Preliminary evidence of cognitive and brain abnormalities in uncomplicated adolescent obesity. Obesity (Silver Spring), 22(8), 18651871. doi:10.1002/oby.20801 Google Scholar
Yau, P.L., Kim, M., Tirsi, A., & Convit, A. (2014). Retinal vessel alterations and cerebral white matter microstructural damage in obese adolescents with metabolic syndrome. JAMA Pediatrics, 168(12), e142815. doi:10.1001/jamapediatrics.2014.2815 Google Scholar