Advertisement

Association of copper levels in the hair with gray matter volume, mean diffusivity, and cognitive functions

  • Hikaru TakeuchiEmail author
  • Yasuyuki Taki
  • Rui Nouchi
  • Ryoichi Yokoyama
  • Yuka Kotozaki
  • Seishu Nakagawa
  • Atsushi Sekiguchi
  • Kunio Iizuka
  • Yuki Yamamoto
  • Sugiko Hanawa
  • Tsuyoshi Araki
  • Carlos Makoto Miyauchi
  • Kohei Sakaki
  • Takayuki Nozawa
  • Shigeyuki Ikeda
  • Susumu Yokota
  • Magistro Daniele
  • Yuko Sassa
  • Ryuta Kawashima
Original Article
  • 79 Downloads

Abstract

Although copper plays a critical role in normal brain functions and development, it is known that excess copper causes toxicity. Here we investigated the associations of copper levels in the hair with regional gray matter volume (rGMV), mean diffusivity (MD), and cognitive differences in a study cohort of 924 healthy young adults. Our findings showed that high copper levels were associated mostly with low cognitive abilities (low scores on the intelligence test consisting of complex speed tasks, involving reasoning task, a complex arithmetic task, and a reading comprehension task) as well as lower reverse Stroop interference, high rGMV over widespread areas of the brain [mainly including the bilateral lateral and medial parietal cortices, medial temporal structures (amygdala, hippocampus, and parahippocampal gyrus), middle cingulate cortex, orbitofrontal cortex, insula, perisylvian areas, inferior temporal lobe, temporal pole, occipital lobes, and supplementary motor area], as well as high MD of the right substantia nigra and bilateral hippocampus, which are indicative of low density in brain tissues. These results suggest that copper levels are associated with mostly aberrant cognitive functions, greater rGMV in extensive areas, greater MD (which are indicative of low density in brain tissues) in subcortical structures in the healthy young adults, possibly reflecting copper’s complex roles in neural mechanisms.

Keywords

Copper Regional gray matter volume Mean diffusivity Cognitive functions 

Notes

Acknowledgements

We respectfully thank Yuki Yamada for operating the MRI scanner, and Haruka Nouchi for being an examiner of psychological tests. We also thank study participants, the other examiners of psychological tests, and all of our colleagues in Institute of Development, Aging and Cancer and in Tohoku University for their support. This study was supported by a Grant-in-Aid for Young Scientists (B) (KAKENHI 23700306) and a Grant-in-Aid for Young Scientists (A) (KAKENHI 25700012) from the Ministry of Education, Culture, Sports, Science, and Technology. The authors would like to thank Enago (http://www.enago.jp) for the English language review. We would like to thank La Belle Vie Inc. and its employees for the hair mineral level analyses as well as Dr. Yasuda and Dr. Sonobe for their technical advice regarding the analyses.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

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

Supplementary material

429_2019_1830_MOESM1_ESM.docx (196 kb)
Supplementary material 1 (DOCX 196 KB)
429_2019_1830_MOESM2_ESM.tif (113 kb)
Supplementary material 2 (TIF 112 KB)

References

  1. Ashburner J, Friston KJ (2000) Voxel-based morphometry-the methods. Neuroimage 11:805–821CrossRefGoogle Scholar
  2. Ayodele J, Bayero A (2009) Lead and zinc concentrations in hair and nail of some Kano inhabitants. African J Env Sci Tech 3:164–170Google Scholar
  3. Bai X, Wang G, Wu L, Liu Y, Cui L, Shi H, Guo L (2013) Deep-gray nuclei susceptibility-weighted imaging filtered phase shift in patients with Wilson’s disease. Pediatr Res 75:436–442CrossRefGoogle Scholar
  4. Bass DA, Hickok D, Quig D, Urek K (2001) Trace element analysis in hair: factors determining accuracy, precision, and reliability. Altern Med Rev 6:472–481Google Scholar
  5. Benjamini Y, Hochberg Y (2000) On the adaptive control of the false discovery rate in multiple testing with independent statistics. J Educ Behav Stat 25:60–83CrossRefGoogle Scholar
  6. Brewer GJ (2009) Risks of copper and iron toxicity during aging in humans. Chem Res Toxicol 23:319–326CrossRefGoogle Scholar
  7. Brewer GJ (2012) Copper toxicity in Alzheimer’s disease: cognitive loss from ingestion of inorganic copper. J Trace Elem Med Biol 26:89–92CrossRefGoogle Scholar
  8. Castell A, Bowland J (1968) Supplemental copper for swine: Effects upon hemoglobin, serum proteins and tissue copper levels. Can J Anim Sci 48:415–424CrossRefGoogle Scholar
  9. Cecil KM, Brubaker CJ, Adler CM, Dietrich KN, Altaye M, Egelhoff JC, Wessel S, Elangovan I, Hornung R, Jarvis K (2008) Decreased brain volume in adults with childhood lead exposure. PLoS Med 5:e112CrossRefGoogle Scholar
  10. Chandarana H, Do RK, Mussi TC, Jensen JH, Hajdu CH, Babb JS, Taouli B (2012) The effect of liver iron deposition on hepatic apparent diffusion coefficient values in cirrhosis. Am J Roentgenol 199:803–808CrossRefGoogle Scholar
  11. Chang CS, Choi JB, Kim HJ, Park SB (2011) Correlation between serum testosterone level and concentrations of copper and zinc in hair tissue. Biol Trace Elem Res 144:264–271CrossRefGoogle Scholar
  12. Chłopicka J, Zachwieja Z, Zagrodzki P, Frydrych J, Słota P, Krośniak M (1998) Lead and cadmium in the hair and blood of children from a highly industrial area in Poland. Biol Trace Elem Res 62:229–234CrossRefGoogle Scholar
  13. Cloninger CR, Svrakic DM, Przybeck TR (1993) A psychobiological model of temperament and character. Arch Gen Psychiatry 50:975–990CrossRefGoogle Scholar
  14. Costa PT, McCrae RR (1992) Professional manual: revised NEO personality inventory (NEO-PI-R) and NEO five-factor inventory (NEO-FFI). Psychological Assessment Resources, OdessaGoogle Scholar
  15. Desai V, Kaler SG (2008) Role of copper in human neurological disorders. Am J Clin Nutr 88:855S–858SCrossRefGoogle Scholar
  16. Everson G, Tsai H, Wang T (1967) Copper deficiency in the guinea pig. J Nutr 93:533–540CrossRefGoogle Scholar
  17. Fox PL (2003) The copper-iron chronicles: the story of an intimate relationship. Biometals 16:9–40CrossRefGoogle Scholar
  18. Frieden E (1980) Caeruloplasmin: a multi-functional metalloprotein of vertebrate plasma. In: Biological roles of copper, pp 93–124: Excerpta Medica AmsterdamGoogle Scholar
  19. Friedman S, Kaufman S (1965) 3, 4-dihydroxyphenylethylamine beta-hydroxylase. Physical properties, copper content, and role of copper in the catalytic acttivity. J Biol Chem 240:4763–4773Google Scholar
  20. Gaetke LM, Chow CK (2003) Copper toxicity, oxidative stress, and antioxidant nutrients. Toxicology 189:147–163CrossRefGoogle Scholar
  21. Ha J-H, Doguer C, Flores SR, Wang T, Collins JF (2018) Progressive increases in dietary iron are associated with the emergence of pathologic disturbances of copper homeostasis in growing rats. J Nutr 148:373–378CrossRefGoogle Scholar
  22. Hakoda Y, Sasaki M (1990) Group version of the Stroop and reverse-Stroop test: the effects of reaction mode, order and practice. Kyoikushinrigakukenkyu (Educational Psychology Research) 38:389–394Google Scholar
  23. Hashimoto M (2008) Alzheimer’s disease and nutrition, especially copper, zinc and docosahexaenoic acid. Trace Nutr Res 25:8–18Google Scholar
  24. Hashimoto T, Takeuchi H, Taki Y, Sekiguchi A, Nouchi R, Kotozaki Y, Nakagawa S, Miyauchi CM, Iizuka K, Yokoyama R (2015) Neuroanatomical correlates of the sense of control: Gray and white matter volumes associated with an internal locus of control. Neuroimage 119:146–151CrossRefGoogle Scholar
  25. Hunt D (1980) Copper and neurological function. Biol Roles Copper 7:247–266Google Scholar
  26. Hunt CD, Idso JP (1995) Moderate copper deprivation during gestation and lactation affects dentate gyrus and hippocampal maturation in immature male rats. J Nutr 125:2700–2710Google Scholar
  27. Jacob RA, Klevay L, Logan G Jr (1978) Hair as a biopsy material V. Hair metal as an index of hepatic metal in rats: copper and zinc. Am J Clin Nutr 31:477–480CrossRefGoogle Scholar
  28. Johansen-Berg H, Baptista CS, Thomas AG (2012) Human structural plasticity at record speed. Neuron 73:1058–1060CrossRefGoogle Scholar
  29. Jung RE, Segall JM, Jeremy Bockholt H, Flores RA, Smith SM, Chavez RS, Haier RJ (2010) Neuroanatomy of creativity. Hum Brain Mapp 31:398–409Google Scholar
  30. Kedzierska E (2003) Concentrations of selected bioelements and toxic metals and their influence on health status of children and youth residing in Szczecin]. Ann Acad Med Stetin 49:131–143Google Scholar
  31. Kijima N, Saito R, Takeuchi M, Yoshino A, Ono Y, Kato M, Kitamura T (1996) Cloninger-no-kishitsu-to-seikaku-no-7inshimodel-oyobi-nihongoban [Cloninger’s seven-factor model of temperament and character and Japanese version of Temperament and Character Inventory (TCI)]. Seishinka-shindangaku [Archives of Psychiatric. Diagn Clin Evaluat 7:379–399Google Scholar
  32. Koc ER, Ilhan A, Aytürk Z, Acar B, GÜRLER M, Karapirli ALTUNTAŞA, Bodur M AS (2015) A comparison of hair and serum trace elements in patients with Alzheimer disease and healthy participants. Turkish J Med Sci 45:1034–1039CrossRefGoogle Scholar
  33. Kondo H, Morishita M, Ashida K, Osaka N (2003) Reading comprehension and working memory–structural equation modeling approach. Jpn J Psychol 73:480–487CrossRefGoogle Scholar
  34. Krejpcio Z, Olejnik D, Wójciak R, Kielczewska K, Gawęcki J (1997) Assessment of the content of calcium, magnesium, zinc and copper in hair and serum of children with hyperactivity. Polish J Environ Stud 6:89–92Google Scholar
  35. Kriegeskorte N, Simmons WK, Bellgowan PS, Baker CI (2009) Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci 12:535–540CrossRefGoogle Scholar
  36. Lech T (2002) Lead, copper, zinc, and magnesium content in hair of children and young people with some neurological diseases. Biol Trace Elem Res 85:111–126CrossRefGoogle Scholar
  37. Loef M, Walach H (2012) Copper and iron in Alzheimer’s disease: a systematic review and its dietary implications. Br J Nutr 107:7–19CrossRefGoogle Scholar
  38. Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH (2003) An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 19:1233–1239CrossRefGoogle Scholar
  39. Maldjian JA, Laurienti PJ, Burdette JH (2004) Precentral gyrus discrepancy in electronic versions of the Talairach atlas. Neuroimage 21:450–455CrossRefGoogle Scholar
  40. Manto M (2014) Abnormal copper homeostasis: mechanisms and roles in neurodegeneration. Toxics 2:327–345CrossRefGoogle Scholar
  41. McNair DM, Lorr M, Droppleman LF (1992) Profile of mood states. Educational and Industrial Testing Service, San DiegoGoogle Scholar
  42. Mikulewicz M, Chojnacka K, Gedrange T, Górecki H (2013) Reference values of elements in human hair: a systematic review. Environ Toxicol Pharmacol 36:1077–1086CrossRefGoogle Scholar
  43. Morris MC, Evans DA, Tangney CC, Bienias JL, Schneider JA, Wilson RS, Scherr PA (2006) Dietary copper and high saturated and trans fat intakes associated with cognitive decline. Arch Neurol 63:1085–1088CrossRefGoogle Scholar
  44. Nolan KR (1983) Copper toxicity syndrome. J Orthomol Psychiatry 12:270–282Google Scholar
  45. Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9:97–113CrossRefGoogle Scholar
  46. Pal A, Siotto M, Prasad R, Squitti R (2015) Towards a unified vision of copper involvement in Alzheimer’s disease: a review connecting basic, experimental, and clinical research. J Alzheimer’s Dis 44:343–354CrossRefGoogle Scholar
  47. Paris I, Dagnino-Subiabre A, Marcelain K, Bennett LB, Caviedes P, Caviedes R, Azar CO, Segura-Aguilar J (2001) Copper neurotoxicity is dependent on dopamine-mediated copper uptake and one-electron reduction of aminochrome in a rat substantia nigra neuronal cell line. J Neurochem 77:519–529CrossRefGoogle Scholar
  48. Péran P, Cherubini A, Assogna F, Piras F, Quattrocchi C, Peppe A, Celsis P, Rascol O, Démonet J-F, Stefani A (2010) Magnetic resonance imaging markers of Parkinson’s disease nigrostriatal signature. Brain 133:3423–3433CrossRefGoogle Scholar
  49. Pfeiffer CC, Mailloux R (1987) Excess copper as a factor in human diseases. J Orthomol Med 2:171–182Google Scholar
  50. Priya MDL, Geetha A (2011) Level of trace elements (copper, zinc, magnesium and selenium) and toxic elements (lead and mercury) in the hair and nail of children with autism. Biol Trace Elem Res 142:148–158CrossRefGoogle Scholar
  51. Rahman MA, Azad MAK, Hossain MI, Qusar MS, Bari W, Begum F, Huq SI, Hasnat A (2009) Zinc, manganese, calcium, copper, and cadmium level in scalp hair samples of schizophrenic patients. Biol Trace Elem Res 127:102–108CrossRefGoogle Scholar
  52. Raven J (1998) Manual for Raven’s progressive matrices and vocabulary scales. Oxford Psychologists Press, OxfordGoogle Scholar
  53. Razek AA, Elmongy A, Hazem M, Zakareyia S, Gabr W (2011) Idiopathic Parkinson disease effect of levodopa on apparent diffusion coefficient value of the brain. Acad Radiol 18:70–73CrossRefGoogle Scholar
  54. Rimland B, Larson GE (1983) Hair mineral analysis and behavior an analysis of 51 studies. J Learn Disabil 16:279–285CrossRefGoogle Scholar
  55. Sagi Y, Tavor I, Hofstetter S, Tzur-Moryosef S, Blumenfeld-Katzir T, Assaf Y (2012) Learning in the fast lane: new insights into neuroplasticity. Neuron 73:1195–1203CrossRefGoogle Scholar
  56. Sakai A (1970) Iron, copper, and zinc content of human hair. Nippon Eiseigaku Zasshi 25:420–437CrossRefGoogle Scholar
  57. Salustri C, Barbati G, Ghidoni R, Quintiliani L, Ciappina S, Binetti G, Squitti R (2010) Is cognitive function linked to serum free copper levels? A cohort study in a normal population. Clin Neurophysiol 121:502–507CrossRefGoogle Scholar
  58. Sasaki M, Hakoda Y (1985) The group version of the Stroop and reverse-Stroop test (1). In: Proceedings of Japan Educational Psyhological 27th annual meeting, p 208Google Scholar
  59. Sasaki M, Hakoda Y, Yamagami R (1993) Schizophrenia and reverse-Stroop interference in the group version of the Stroop and reverse-Stroop test. Jpn J Psychol 64:43–50CrossRefGoogle Scholar
  60. Segura-Aguilar J, Metodiewa D, Baez S (2001) The possible role of one-electron reduction of aminochrome in the neurodegenerative process of the dopaminergic system. Neurotox Res 3:157–165CrossRefGoogle Scholar
  61. Seppi K, Schocke MF, Donnemiller E, Esterhammer R, Kremser C, Scherfler C, Diem A, Jaschke W, Wenning GK, Poewe W (2004) Comparison of diffusion-weighted imaging and [123I] IBZM-SPECT for the differentiation of patients with the Parkinson variant of multiple system atrophy from those with Parkinson’s disease. Mov Disord 19:1438–1445CrossRefGoogle Scholar
  62. Shaw P, Greenstein D, Lerch J, Clasen L, Lenroot R, Gogtay N, Evans A, Rapoport J, Giedd J (2006) Intellectual ability and cortical development in children and adolescents. Nature 440:676–679CrossRefGoogle Scholar
  63. Smith SM, Nichols TE (2009) Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage 44:83–98CrossRefGoogle Scholar
  64. Society_For_Creative_Minds (1969) Manual of S-A creativity test. Tokyo shinri Corporation, TokyoGoogle Scholar
  65. Squitti R, Rossini P, Cassetta E-p, Moffa F, Pasqualetti P, Cortesi M, Colloca A, Rossi L (2002) d-penicillamine reduces serum oxidative stress in Alzheimer’s disease patients. Eur J Clin Invest 32:51–59CrossRefGoogle Scholar
  66. Squitti R, Bressi F, Pasqualetti P, Bonomini C, Ghidoni R, Binetti G, Cassetta E, Moffa F, Ventriglia M, Vernieri F (2009) Longitudinal prognostic value of serum “free” copper in patients with Alzheimer disease. Neurology 72:50–55CrossRefGoogle Scholar
  67. Squitti R, Siotto M, Polimanti R (2014a) Low-copper diet as a preventive strategy for Alzheimer’s disease. Neurobiol Aging 35:S40–S50CrossRefGoogle Scholar
  68. Squitti R, Simonelli I, Ventriglia M, Siotto M, Pasqualetti P, Rembach A, Doecke J, Bush AI (2014b) Meta-analysis of serum non-ceruloplasmin copper in Alzheimer’s disease. J Alzheimer’s Dis 38:809–822CrossRefGoogle Scholar
  69. Takeuchi H, Kawashima R (2016) Neural mechanisms and children’s intellectual development: multiple impacts of environmental factors. Neuroscientist 22:618–631CrossRefGoogle Scholar
  70. Takeuchi H, Kawashima R (2018) Mean diffusivity in the dopaminergic system and neural differences related to dopaminergic system. Curr Neuropharmacol 16:460–474CrossRefGoogle Scholar
  71. Takeuchi H, Taki Y, Sassa Y, Hashizume H, Sekiguchi A, Fukushima A, Kawashima R (2010) Regional gray matter volume of dopaminergic system associate with creativity: evidence from voxel-based morphometry. Neuroimage 51:578–585CrossRefGoogle Scholar
  72. Takeuchi H, Taki Y, Sassa Y, Hashizume H, Sekiguchi A, Fukushima A, Kawashima R (2011a) Working memory training using mental calculation impacts regional gray matter of the frontal and parietal regions. PLoS One 6:e23175CrossRefGoogle Scholar
  73. Takeuchi H, Taki Y, Hashizume H, Sassa Y, Nagase T, Nouchi R, Kawashima R (2011b) Failing to deactivate: the association between brain activity during a working memory task and creativity. Neuroimage 55:681–687CrossRefGoogle Scholar
  74. Takeuchi H, Taki Y, Hashizume H, Sassa Y, Nagase T, Nouchi R, Kawashima R (2012) The association between resting functional connectivity and creativity. Cereb Cortex 22:2921–2929CrossRefGoogle Scholar
  75. Takeuchi H, Taki Y, Nouchi R, Hashizume H, Sekiguchi A, Kotozaki Y, Nakagawa S, Miyauchi CM, Sassa Y, Kawashima R (2013a) Effects of working memory-training on functional connectivity and cerebral blood flow during rest. Cortex 49:2106–2125CrossRefGoogle Scholar
  76. Takeuchi H, Taki Y, Thyreau B, Sassa Y, Hashizume H, Sekiguchi A, Nagase T, Nouchi R, Fukushima A, Kawashima R (2013b) White matter structures associated with empathizing and systemizing in young adults. Neuroimage 77:222–236CrossRefGoogle Scholar
  77. Takeuchi H, Taki Y, Sekiguchi A, Nouchi R, Kotozaki Y, Nakagawa S, Miyauchi CM, Iizuka K, Yokoyama R, Shinada T, Yamamoto Y, Hanawa S, Araki T, Hashizume H, Sassa Y, Kawashima R (2013c) Association of hair iron levels with creativity and psychological variables related to creativity. Front Hum Neurosci 7:1–9CrossRefGoogle Scholar
  78. Takeuchi H, Taki Y, Nouchi R, Hashizume H, Sekiguchi A, Kotozaki Y, Nakagawa S, Miyauchi CM, Sassa Y, Kawashima R (2015a) Working memory training impacts the mean diffusivity in the dopaminergic system. Brain Struct Funct 220:3101–3111CrossRefGoogle Scholar
  79. Takeuchi H, Taki Y, Sekuguchi A, Hashizume H, Nouchi R, Sassa Y, Kotozaki Y, Miyauchi CM, Yokoyama R, Iizuka K, Nakagawa S, Nagase T, Kunitoki K, Kawashima R (2015b) Mean diffusivity of globus pallidus associated with verbal creativity measured by divergent thinking and creativity-related temperaments in young healthy adults. Hum Brain Mapp 36:1808–1827CrossRefGoogle Scholar
  80. Takeuchi H, Taki Y, Nouchi R, Sekiguchi A, Hashizume H, Sassa Y, Kotozaki Y, Miyauchi CM, Yokoyama R, Iizuka K, Seishu N, Tomomi N, Kunitoki K, Kawashima R (2015c) Degree centrality and fractional amplitude of low-frequency oscillations associated with Stroop interference. Neuroimage 119:197–209CrossRefGoogle Scholar
  81. Takeuchi H, Taki Y, Sekiguchi A, Nouchi R, Kotozaki Y, Nakagawa S, Miyauchi CM, Iizuka K, Yokoyama R, Shinada T, Yamamoto Y, Hanawa S, Araki T, Hashizume H, Sassa Y, Kawashima R (2015d) Brain structures in the sciences and humanities. Brain Struct Funct 220:3295–3305CrossRefGoogle Scholar
  82. Takeuchi H, Taki Y, Hashizume H, Asano K, Asano M, Sassa Y, Yokota S, Kotozaki Y, Nouchi R, Kawashima R (2016a) Impact of videogame play on the brain’s microstructural properties: cross-sectional and longitudinal analyses. Mol Psychiatry 21:1781–1789CrossRefGoogle Scholar
  83. Takeuchi H, Taki Y, Sekiguchi A, Nouchi R, Kotozaki Y, Nakagawa S, Miyauchi CM, Iizuka K, Yokoyama R, Shinada T (2016b) Mean diffusivity of basal ganglia and thalamus specifically associated with motivational states among mood states. Brain Struct Funct:1–11Google Scholar
  84. Takeuchi H, Taki Y, Nouchi R, Yokoyama R, Kotozaki Y, Nakagawa S, Sekiguchi A, Iizuka K, Yamamoto Y, Hanawa S, Araki T, Miyauchi CM, Shinada T, Sakaki K, Sassa Y, Nozawa T, Ikeda S, Yokota S, Daniele M, Kawashima R (2017) Creative females have larger white matter structures: evidence from a large sample study. Hum Brain Mapp 38:414–430CrossRefGoogle Scholar
  85. Taki Y, Hashizume H, Sassa Y, Takeuchi H, Wu K, Asano M, Asano K, Fukuda H, Kawashima R (2011) Correlation between gray matter density-adjusted brain perfusion and age using brain MR images of 202 healthy children. Hum Brain Mapp 32:1973–1985CrossRefGoogle Scholar
  86. Tanaka K, Okamoto K, Tanaka H (2003) Manual of new tanaka B type intelligence test. Kaneko Syobo, TokyoGoogle Scholar
  87. Tobi EW, Goeman JJ, Monajemi R, Gu H, Putter H, Zhang Y, Slieker RC, Stok AP, Thijssen PE, Müller F (2014) DNA methylation signatures link prenatal famine exposure to growth and metabolism. Nat Commun 5:5592CrossRefGoogle Scholar
  88. Turnlund JR, Jacob RA, Keen CL, Strain J, Kelley DS, Domek JM, Keyes WR, Ensunsa JL, Lykkesfeldt J, Coulter J (2004) Long-term high copper intake: effects on indexes of copper status, antioxidant status, and immune function in young men. Am J Clin Nutr 79:1037–1044CrossRefGoogle Scholar
  89. 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 15:273–289CrossRefGoogle Scholar
  90. Vir SC, Love A (1981) Zinc and copper nutriture of women taking oral contraceptive agents. Am J Clin Nutr 34:1479–1483CrossRefGoogle Scholar
  91. Wilson L (2003) Nutritional balancing and hair tissue mineral analysis. Explore-Mount Vernon 12:42–53Google Scholar
  92. Yokoyama K (2005) POMS Shortened Version (in Japanese). Kanekoshobo, TokyoGoogle Scholar
  93. Zecca L, Zucca F, Toscani M, Adorni F, Giaveri G, Rizzio E, Gallorini M (2005) Iron, copper and their proteins in substantia nigra of human brain during aging. J Radioanal Nuclear Chem 263:733–737CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Hikaru Takeuchi
    • 1
    Email author
  • Yasuyuki Taki
    • 1
    • 2
    • 3
  • Rui Nouchi
    • 4
    • 5
    • 6
  • Ryoichi Yokoyama
    • 7
  • Yuka Kotozaki
    • 8
  • Seishu Nakagawa
    • 9
    • 10
  • Atsushi Sekiguchi
    • 2
    • 11
  • Kunio Iizuka
    • 12
  • Yuki Yamamoto
    • 9
  • Sugiko Hanawa
    • 9
  • Tsuyoshi Araki
    • 13
  • Carlos Makoto Miyauchi
    • 14
  • Kohei Sakaki
    • 6
  • Takayuki Nozawa
    • 15
  • Shigeyuki Ikeda
    • 16
  • Susumu Yokota
    • 1
  • Magistro Daniele
    • 17
  • Yuko Sassa
    • 1
  • Ryuta Kawashima
    • 1
    • 6
    • 9
  1. 1.Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and CancerTohoku UniversitySendaiJapan
  2. 2.Division of Medical Neuroimaging Analysis, Department of Community Medical Supports, Tohoku Medical Megabank OrganizationTohoku UniversitySendaiJapan
  3. 3.Department of Radiology and Nuclear Medicine, Institute of Development, Aging and CancerTohoku UniversitySendaiJapan
  4. 4.Creative Interdisciplinary Research Division, Frontier Research Institute for Interdisciplinary ScienceTohoku UniversitySendaiJapan
  5. 5.Human and Social Response Research Division, International Research Institute of Disaster ScienceTohoku UniversitySendaiJapan
  6. 6.Department of Advanced Brain Science, Institute of Development, Aging and CancerTohoku UniversitySendaiJapan
  7. 7.School of MedicineKobe UniversityKobeJapan
  8. 8.Division of Clinical research, Medical-Industry Translational Research CenterFukushima Medical University School of MedicineFukushimaJapan
  9. 9.Department of Human Brain Science, Institute of Development, Aging and CancerTohoku UniversitySendaiJapan
  10. 10.Division of PsychiatryTohoku Medical and Pharmaceutical UniversitySendaiJapan
  11. 11.Department of Behavioral MedicineNational Institute of Mental Health, National Center of Neurology and PsychiatryTokyoJapan
  12. 12.Department of PsychiatryTohoku University Graduate School of MedicineSendaiJapan
  13. 13.ADVANTAGE Risk Management Co., LtdTokyoJapan
  14. 14.Department of Language Sciences, Graduate School of HumanitiesTokyo Metropolitan UniversityTokyoJapan
  15. 15.Collaborative Research Center for Happiness Co-Creation Society through Intelligent CommunicationsTokyo Institute of TechnologyTokyoJapan
  16. 16.Department of Ubiquitous Sensing, Institute of Development, Aging and CancerTohoku UniversitySendaiJapan
  17. 17.Department of Sport Science, School of Science and TechnologyNottingham Trent UniversityNottinghamUK

Personalised recommendations