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Viscoelasticity of striatal brain areas reflects variations in body mass index of lean to overweight male adults

  • Stefan Hetzer
  • Sebastian Hirsch
  • Jürgen Braun
  • Ingolf SackEmail author
  • Martin WeygandtEmail author
Original Research
  • 11 Downloads

Abstract

Although a variety of MRI studies investigated the link between body mass index (BMI) and parameters of neural gray matter (GM), the technique applied in most of these studies, voxel-based morphometry (VBM), focusses on the regional GM volume, a macroscopic tissue property. Thus, the studies were not able to exploit the BMI-related information contained in the GM microstructure although PET studies suggest that these factors are important. Here, we used cerebral MR Elastography (MRE) to characterize features of tissue microstructure by evaluating the propagation of shear waves applied to the skull and to assess local tissue viscoelasticity to test the link between this parameter and BMI in 22 lean to overweight males. Unlike the majority of existing MRE studies investigating neural viscoelasticity signals averaged across large brain regions, we used the viscoelasticity of individual voxels for our experiment. Our technique revealed a negative link between BMI and viscoelasticity of two areas of the striatal reward system, i.e., right putamen (t = −8.2; pFWE-corrected = 0.005) and left globus pallidus (t = −7.1; pFWE = 0.037) which was independent of GM volume at these coordinates. Finally, comparison of BMI models based on individual voxels vs. on signals averaged across brain atlas regions demonstrates that voxel-based models explain a significantly higher proportion of variance. Consequently, our findings show that cerebral MRE is suitable to identify medically relevant microstructural tissue properties. Using a voxel-wise analysis approach, we were able to utilize the high spatial resolution of MRE for mapping BMI-related information in the brain.

Keywords

Reward system Magnetic resonance elastography Viscoelasticity Body mass index BMI Quantitative MRI 

Notes

Acknowledgements

The authors would like to thank Patric Birr for his valuable help during data acquisition and Thomas Christophel for fruitful discussions.

Funding information

IS and JB received funding from the European Union’s Horizon 2020 Programme (ID 668039, EU FORCE – Imaging the Force of Cancer) and from the German Research Foundation (grant no. Sa 901/16, GRK2260 BIOQIC, SFB1340 Matrix-in-Vision). MW received funding from the German Research Foundation (WE 5967/2-1).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical statement

The study was approved by the ethical review board of the Charité – Universitätsmedizin Berlin in conformity with the Declaration of Helsinki. All procedures performed in this study were in accordance with existing ethical standards. All authors have been personally and actively involved in substantial work leading to the manuscript.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Andersson, J. L. R., Skare, S., & Ashburner, J. (2003). How to correct susceptibility distortions in spin-echo echo-planar images: Application to diffusion tensor imaging. NeuroImage, 20, 870–888.  https://doi.org/10.1016/S1053-8119(03)00336-7.CrossRefGoogle Scholar
  2. Arani, A., Murphy, M. C., Glaser, K. J., Manduca, A., Lake, D. S., Kruse, S. A., Jack, C. R., Jr., Ehman, R. L., & Huston, J., 3rd. (2015). Measuring the effects of aging and sex on regional brain stiffness with MR elastography in healthy older adults. NeuroImage, 111, 59–64.  https://doi.org/10.1016/j.neuroimage.2015.02.016.CrossRefGoogle Scholar
  3. Barnhill, E., Hollis, L., Sack, I., Braun, J., Hoskins, P. R., Pankaj, P., Brown, C., van Beek, E. J. R., & Roberts, N. (2017). Nonlinear multiscale regularisation in MR elastography: Towards fine feature mapping. Medical Image Analysis, 35, 133–145.  https://doi.org/10.1016/j.media.2016.05.012.CrossRefGoogle Scholar
  4. Berridge, K. C., Robinson, T. E., & Aldridge, J. W. (2009). Dissecting components of reward: “Liking”, “wanting”, and learning. Current Opinion in Pharmacology, 9, 65–73.  https://doi.org/10.1016/j.coph.2008.12.014.CrossRefGoogle Scholar
  5. Braun, J., Guo, J., Lützkendorf, R., Stadler, J., Papazoglou, S., Hirsch, S., Sack, I., & Bernarding, J. (2014). High-resolution mechanical imaging of the human brain by three-dimensional multifrequency magnetic resonance elastography at 7T. NeuroImage, 90, 308–314.  https://doi.org/10.1016/j.neuroimage.2013.12.032.CrossRefGoogle Scholar
  6. Bray, G. A. (2004). Medical consequences of obesity. The Journal of Clinical Endocrinology and Metabolism, 89, 2583–2589.  https://doi.org/10.1210/jc.2004-0535.CrossRefGoogle Scholar
  7. Chaze, C. A., McIlvain, G., Smith, D. R., Villermaux, G. M., Delgorio, P. L., Wright, H. G., Rogers, K. J., Miller, F., Crenshaw, J. R., & Johnson, C. L. (2019). Altered brain tissue viscoelasticity in pediatric cerebral palsy measured by magnetic resonance elastography. NeuroImage Clinical, 22, 101750.  https://doi.org/10.1016/j.nicl.2019.101750.
  8. Christensen, R. (2011). Plane answers to complex questions: The theory of linear models. New York: Springer.CrossRefGoogle Scholar
  9. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, N.J: L. Erlbaum Associates.Google Scholar
  10. Dittmann, F., Hirsch, S., Tzschätzsch, H., Guo, J., Braun, J., & Sack, I. (2015). In vivo wideband multifrequency MR elastography of the human brain and liver. Magnetic Resonance in Medicine, 76, 1116–1126.  https://doi.org/10.1002/mrm.26006.CrossRefGoogle Scholar
  11. Fehlner, A., Papazoglou, S., McGarry, M. D., et al. (2015). Cerebral multifrequency MR elastography by remote excitation of intracranial shear waves. NMR in Biomedicine, 28, 1426–1432.  https://doi.org/10.1002/nbm.3388.CrossRefGoogle Scholar
  12. Fehlner, A., Hirsch, S., Weygandt, M., Christophel, T., Barnhill, E., Kadobianskyi, M., Braun, J., Bernarding, J., Lützkendorf, R., Sack, I., & Hetzer, S. (2016). Increasing the spatial resolution and sensitivity of magnetic resonance elastography by correcting for subject motion and susceptibility-induced image distortions. Journal of Magnetic Resonance Imaging, 46, 134–141.  https://doi.org/10.1002/jmri.25516.
  13. Flegal, K. M., Kit, B. K., Orpana, H., & Graubard, B. I. (2013). Association of all-cause mortality with overweight and obesity using standard body mass index categories: A systematic review and meta-analysis. JAMA, 309, 71–82.  https://doi.org/10.1001/jama.2012.113905.CrossRefGoogle Scholar
  14. Freimann, F. B., Müller, S., Streitberger, K.-J., Guo, J., Rot, S., Ghori, A., Vajkoczy, P., Reiter, R., Sack, I., & Braun, J. (2013). MR elastography in a murine stroke model reveals correlation of macroscopic viscoelastic properties of the brain with neuronal density. NMR in Biomedicine, 26, 1534–1539.  https://doi.org/10.1002/nbm.2987.CrossRefGoogle Scholar
  15. Gerischer, L. M., Fehlner, A., Köbe, T., Prehn, K., Antonenko, D., Grittner, U., Braun, J., Sack, I., & Flöel, A. (2018). Combining viscoelasticity, diffusivity and volume of the hippocampus for the diagnosis of Alzheimer’s disease based on magnetic resonance imaging. NeuroImage Clinical, 18, 485–493.  https://doi.org/10.1016/j.nicl.2017.12.023.
  16. Guo, J., Hirsch, S., Fehlner, A., Papazoglou, S., Scheel, M., Braun, J., & Sack, I. (2013). Towards an elastographic atlas of brain anatomy. PLoS One, 8, e71807.  https://doi.org/10.1371/journal.pone.0071807.CrossRefGoogle Scholar
  17. Hetzer, S., Birr, P., Fehlner, A., Hirsch, S., Dittmann, F., Barnhill, E., Braun, J., & Sack, I. (2017). Perfusion alters stiffness of deep gray matter. Journal of Cerebral Blood Flow & Metabolism, 38(1), 116–125.  https://doi.org/10.1177/0271678X17691530.
  18. Hetzer, S., Dittmann, F., Bormann, K., Hirsch, S., Lipp, A., Wang, D. J. J., Braun, J., & Sack, I. (2018). Hypercapnia increases brain viscoelasticity. Journal of Cerebral Blood Flow & Metabolism, 0271678X18799241.  https://doi.org/10.1177/0271678X18799241.
  19. Hirsch, S., Klatt, D., Freimann, F., Scheel, M., Braun, J., & Sack, I. (2013). In vivo measurement of volumetric strain in the human brain induced by arterial pulsation and harmonic waves. Magnetic Resonance in Medicine, 70, 671–683.  https://doi.org/10.1002/mrm.24499.CrossRefGoogle Scholar
  20. Hirsch, S., Guo, J., Reiter, R., Papazoglou, S., Kroencke, T., Braun, J., & Sack, I. (2014). MR elastography of the liver and the spleen using a piezoelectric driver, single-shot wave-field acquisition, and multifrequency dual parameter reconstruction. Magnetic Resonance in Medicine, 71, 267–277.  https://doi.org/10.1002/mrm.24674.CrossRefGoogle Scholar
  21. Hirsch, S., Braun, J., & Sack, I. (2017). Magnetic resonance Elastography: Physical background and medical applications. KGaA, Weinheim, Germany: Wiley-VCH Verlag GmbH & Co.Google Scholar
  22. Hiscox, L. V., Johnson, C. L., McGarry, M. D. J., et al. (2018a). High-resolution magnetic resonance elastography reveals differences in subcortical gray matter viscoelasticity between young and healthy older adults. Neurobiology of Aging, 65, 158–167.  https://doi.org/10.1016/j.neurobiolaging.2018.01.010.CrossRefGoogle Scholar
  23. Hiscox, L. V., Johnson, C. L., McGarry, M. D. J., et al. (2018b). Hippocampal viscoelasticity and episodic memory performance in healthy older adults examined with magnetic resonance elastography. Brain Imaging and Behavior.  https://doi.org/10.1007/s11682-018-9988-8.
  24. Ito, R., Everitt, B. J., & Robbins, T. W. (2005). The hippocampus and appetitive Pavlovian conditioning: Effects of excitotoxic hippocampal lesions on conditioned locomotor activity and autoshaping. Hippocampus, 15, 713–721.  https://doi.org/10.1002/hipo.20094.CrossRefGoogle Scholar
  25. Janowitz, D., Wittfeld, K., Terock, J., Freyberger, H. J., Hegenscheid, K., Völzke, H., Habes, M., Hosten, N., Friedrich, N., Nauck, M., Domanska, G., & Grabe, H. J. (2015). Association between waist circumference and gray matter volume in 2344 individuals from two adult community-based samples. NeuroImage, 122, 149–157.  https://doi.org/10.1016/j.neuroimage.2015.07.086.CrossRefGoogle Scholar
  26. Johnson, C. L., Schwarb, H., McGarry, M. D. J., Anderson, A. T., Huesmann, G. R., Sutton, B. P., & Cohen, N. J. (2016). Viscoelasticity of subcortical gray matter structures. Human Brain Mapping, 37, 4221-4233.  https://doi.org/10.1002/hbm.23314.
  27. Johnson, C. L., Schwarb, H., Horecka, K. M., McGarry, M. D. J., Hillman, C. H., Kramer, A. F., Cohen, N. J., & Barbey, A. K. (2018). Double dissociation of structure-function relationships in memory and fluid intelligence observed with magnetic resonance elastography. NeuroImage, 171, 99–106.  https://doi.org/10.1016/j.neuroimage.2018.01.007.CrossRefGoogle Scholar
  28. Kishinevsky, F. I., Cox, J. E., Murdaugh, D. L., Stoeckel, L. E., Cook, E. W., III, & Weller, R. E. (2012). fMRI reactivity on a delay discounting task predicts weight gain in obese women. Appetite, 58, 582–592.  https://doi.org/10.1016/j.appet.2011.11.029.CrossRefGoogle Scholar
  29. Kullmann, S., Callaghan, M. F., Heni, M., Weiskopf, N., Scheffler, K., Häring, H. U., Fritsche, A., Veit, R., & Preissl, H. (2016). Specific white matter tissue microstructure changes associated with obesity. NeuroImage, 125, 36–44.  https://doi.org/10.1016/j.neuroimage.2015.10.006.CrossRefGoogle Scholar
  30. Lampe, L., Zhang, R., Beyer, F., Huhn, S., Kharabian-Masouleh, S., Preusser, S., Bazin, P. L., Schroeter, M. L., Villringer, A., & Witte, A. V. (2019). Visceral obesity relates to deep white matter hyperintensities via inflammation. Annals of Neurology, 85, 194–203.  https://doi.org/10.1002/ana.25396.Google Scholar
  31. Millward, J. M., Guo, J., Berndt, D., Braun, J., Sack, I., & Infante-Duarte, C. (2015). Tissue structure and inflammatory processes shape viscoelastic properties of the mouse brain. NMR in Biomedicine, 28, 831–839.  https://doi.org/10.1002/nbm.3319.CrossRefGoogle Scholar
  32. Murphy, M. C., Manduca, A., Trzasko, J. D., Glaser, K. J., Huston, J., III, & Ehman, R. L. (2018). Artificial neural networks for stiffness estimation in magnetic resonance elastography. Magnetic Resonance in Medicine, 80, 351–360.  https://doi.org/10.1002/mrm.27019.CrossRefGoogle Scholar
  33. Muthupillai, R., Lomas, D. J., Rossman, P. J., Greenleaf, J., Manduca, A., & Ehman, R. (1995). Magnetic resonance elastography by direct visualization of propagating acoustic strain waves. Science, 269, 1854–1857.CrossRefGoogle Scholar
  34. Ng, M., Fleming, T., Robinson, M., et al. (2014). Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet, 384, 766–781.  https://doi.org/10.1016/S0140-6736(14)60460-8.
  35. O’Gorman, T. W. (2005). The performance of randomization tests that use permutations of independent variables. Communications in Statistics: Simulation and Computation, 34, 895–908.  https://doi.org/10.1080/03610910500308230.CrossRefGoogle Scholar
  36. Pannacciulli, N., Del Parigi, A., Chen, K., et al. (2006). Brain abnormalities in human obesity: A voxel-based morphometric study. NeuroImage, 31, 1419–1425.  https://doi.org/10.1016/j.neuroimage.2006.01.047.CrossRefGoogle Scholar
  37. Parpura, V., & Verkhratsky, A. (2012). Homeostatic function of astrocytes: Ca2+ and Na+ signalling. Translational Neuroscience, 3, 334–344.  https://doi.org/10.2478/s13380-012-0040-y.CrossRefGoogle Scholar
  38. Patz, S., Fovargue, D., Schregel, K., Nazari, N., Palotai, M., Barbone, P. E., Fabry, B., Hammers, A., Holm, S., Kozerke, S., Nordsletten, D., & Sinkus, R. (2019). Imaging localized neuronal activity at fast time scales through biomechanics. Science Advances, 5, eaav3816.  https://doi.org/10.1126/sciadv.aav3816.CrossRefGoogle Scholar
  39. Raji, C. A., Ho, A. J., Parikshak, N. N., Becker, J. T., Lopez, O. L., Kuller, L. H., Hua, X., Leow, A. D., Toga, A. W., & Thompson, P. M. (2010). Brain structure and obesity. Human Brain Mapping, 31, 353–364.  https://doi.org/10.1002/hbm.20870.Google Scholar
  40. Rothemund, Y., Preuschhof, C., Bohner, G., Bauknecht, H. C., Klingebiel, R., Flor, H., & Klapp, B. F. (2007). Differential activation of the dorsal striatum by high-calorie visual food stimuli in obese individuals. NeuroImage, 37, 410–421.  https://doi.org/10.1016/j.neuroimage.2007.05.008.CrossRefGoogle Scholar
  41. Ryan, L., & Walther, K. (2014). White matter integrity in older females is altered by increased body fat. Obesity (Silver Spring), 22, 2039–2046.  https://doi.org/10.1002/oby.20815.
  42. Rypma, B., Fischer, H., Rieckmann, A., Hubbard, N. A., Nyberg, L., & Backman, L. (2015). Dopamine D1 binding potential predicts fusiform bold activity during face-recognition performance. Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 35, 14702–14707.  https://doi.org/10.1523/JNEUROSCI.1298-15.2015.CrossRefGoogle Scholar
  43. Sack, I., Beierbach, B., Wuerfel, J., Klatt, D., Hamhaber, U., Papazoglou, S., Martus, P., & Braun, J. (2009). The impact of aging and gender on brain viscoelasticity. NeuroImage, 46, 652–657.  https://doi.org/10.1016/j.neuroimage.2009.02.040.CrossRefGoogle Scholar
  44. Sack, I., Jöhrens, K., Würfel, J., & Braun, J. (2013). Structure-sensitive elastography: On the viscoelastic powerlaw behavior of in vivo human tissue in health and disease. Soft Matter, 9, 5672–5680.  https://doi.org/10.1039/C3SM50552A.CrossRefGoogle Scholar
  45. Schienkiewitz, A., Mensink, G. B. M., & Scheidt-Nave, C. (2012). Comorbidity of overweight and obesity in a nationally representative sample of German adults aged 18-79 years. BMC Public Health, 12, 658.  https://doi.org/10.1186/1471-2458-12-658.CrossRefGoogle Scholar
  46. Schregel, K., Wuerfel, E., Garteiser, P., et al. (2012). Demyelination reduces brain parenchymal stiffness quantified in vivo by magnetic resonance elastography. Proceedings of the National Academy of Sciences of the United States of America, 109, 6650–6655.  https://doi.org/10.1073/pnas.1200151109.CrossRefGoogle Scholar
  47. Schwarb, H., Johnson, C. L., McGarry, M. D. J., & Cohen, N. J. (2016). Medial temporal lobe viscoelasticity and relational memory performance. NeuroImage, 132, 534–541.  https://doi.org/10.1016/j.neuroimage.2016.02.059.CrossRefGoogle Scholar
  48. Streitberger, K.-J., Reiss-Zimmermann, M., Freimann, F. B., Bayerl, S., Guo, J., Arlt, F., Wuerfel, J., Braun, J., Hoffmann, K. T., & Sack, I. (2014). High-resolution mechanical imaging of glioblastoma by multifrequency magnetic resonance elastography. PLoS One, 9, e110588.  https://doi.org/10.1371/journal.pone.0110588.CrossRefGoogle Scholar
  49. Taki, Y., Kinomura, S., Sato, K., Inoue, K., Goto, R., Okada, K., Uchida, S., Kawashima, R., & Fukuda, H. (2008). Relationship between body mass index and gray matter volume in 1,428 healthy individuals. Obes Silver Spring Md, 16, 119–124.  https://doi.org/10.1038/oby.2007.4.CrossRefGoogle Scholar
  50. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15, 273–289.  https://doi.org/10.1006/nimg.2001.0978.CrossRefGoogle Scholar
  51. Volkow, N. D., Wise, R. A., & Baler, R. (2017). The dopamine motive system: Implications for drug and food addiction. Nature Reviews. Neuroscience, 18, 741–752.  https://doi.org/10.1038/nrn.2017.130.CrossRefGoogle Scholar
  52. Wang, G. J., Volkow, N. D., Logan, J., Pappas, N. R., Wong, C. T., Zhu, W., Netusll, N., & Fowler, J. S. (2001). Brain dopamine and obesity. Lancet Lond Engl, 357, 354–357.CrossRefGoogle Scholar
  53. Weise, C. M., Piaggi, P., Reinhardt, M., Chen, K., Savage, C. R., Krakoff, J., & Pleger, B. (2017). The obese brain as a heritable phenotype: A combined morphometry and twin study. International Journal of Obesity 2005, 41, 458–466.  https://doi.org/10.1038/ijo.2016.222.CrossRefGoogle Scholar
  54. Weygandt, M., Mai, K., Dommes, E., Leupelt, V., Hackmack, K., Kahnt, T., Rothemund, Y., Spranger, J., & Haynes, J. D. (2013). The role of neural impulse control mechanisms for dietary success in obesity. NeuroImage, 83, 669–678.  https://doi.org/10.1016/j.neuroimage.2013.07.028.CrossRefGoogle Scholar
  55. Weygandt, M., Mai, K., Dommes, E., Ritter, K., Leupelt, V., Spranger, J., & Haynes, J. D. (2015). Impulse control in the dorsolateral prefrontal cortex counteracts post-diet weight regain in obesity. NeuroImage, 109, 318–327.  https://doi.org/10.1016/j.neuroimage.2014.12.073.CrossRefGoogle Scholar
  56. Weygandt, M., Spranger, J., Leupelt, V., Maurer, L., Bobbert, T., Mai, K., & Haynes, J. D. (2019). Interactions between neural decision-making circuits predict long-term dietary treatment success in obesity. NeuroImage, 184, 520–534.  https://doi.org/10.1016/j.neuroimage.2018.09.058.CrossRefGoogle Scholar
  57. Yokum, S., Ng, J., & Stice, E. (2012). Relation of regional gray and white matter volumes to current BMI and future increases in BMI: A prospective MRI study. International Journal of Obesity 2005, 36, 656–664.  https://doi.org/10.1038/ijo.2011.175.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Berlin Center for Advanced NeuroimagingCharité – UniversitätsmedizinBerlinGermany
  2. 2.Bernstein Center for Computational NeuroscienceBerlinGermany
  3. 3.Institute of Medical InformaticsCharité – Universitätsmedizin BerlinBerlinGermany
  4. 4.Department of RadiologyCharité – Universitätsmedizin BerlinBerlinGermany
  5. 5.Neurocure Excellence ClusterCharité – Universitätsmedizin BerlinBerlinGermany

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