Brain Nucleus Changes in Cognitive Disorders: Examining By the Quantitative Susceptibility Mapping (QSM) Technique

Document Type : Original Paper

Authors

1 Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

2 Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Abstract

Introduction: Iron deposition is vital for damaging neurons and causing different cognitive disorders. Today, using the quantitative susceptibility mapping (QSM) technique, iron deposits in other brain areas can be assessed and measured. This study aimed to identify changes in iron deposition of 12 brain nuclei through different stages of dementia using the QSM technique to introduce biomarkers for the early detection of cognitive disorders.
Material and Methods: The Alzheimer’s Disease Neuroimaging Initiative (ADNI) database was used to download data. A 3T MRI scanner scanned thirty-five participants with normal cognition and forty-six patients with cognitive disorders who were classified into four groups based on the severity of the condition. QSM processing determined twelve regions of interest (ROIs) by automatic nuclei segmentation and statistical analysis performed in these groups’ MRI images.
Results: Based on previous findings, QSM values ​​increase proportionally to iron deposition.
In this study, the increase in the QSM values ​​of different nuclei of the early mild cognitive impairment (EMCI) stage indicates iron deposition in these participants. In the EMCI group, The QSM value of the bilateral thalamus (P<0.05) and left amygdala (P=0.006) nuclei were higher than in the control group. Based on the results of the receiver operating characteristic curve (ROC) analysis, the left amygdala (P=0.005), left putamen (P=0.002), left thalamus (P=0.05), and right thalamus (P<0.05) have an appropriate sensitivity and specificity to identify the different stages of cognitive disorders.
Conclusion: The left amygdala and bilateral thalamic nuclei are the first areas exposed to iron deposition during cognitive impairment. Mentioned nuclei, especially the left amygdala, have high efficiency and sensitivity for the early detection of cognitive disorders.

Keywords

Main Subjects

References

  1. Sosa-Ortiz AL, Acosta-Castillo I, Prince MJ. Epidemiology of dementias and Alzheimer’s disease. Archives of medical research. 2012;43(8):600-8.
  2. Mayeux R, Stern Y. Epidemiology of Alzheimer’s disease. Cold Spring Harbor perspectives in medicine. 2012;2(8):a006239.
  3. Kazemi Y, Houghten S, editors. A deep learning pipeline to classify different stages of Alzheimer’s disease from fMRI data. 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). 2018.
  4. Gong NJ, Chan CC, Leung LM, Wong CS, Dibb R, Liu C. Differential microstructural and morphological abnormalities in mild cognitive impairment and a lzheimer’s disease: Evidence from cortical and deep gray matter. Human brain mapping. 2017;38(5):2495-508.
  5. Gong N-J, Kuzminski S, Clark M, Fraser M, Sundman M, Guskiewicz K, et al. Microstructural alterations of cortical and deep gray matter over a season of high school football revealed by diffusion kurtosis imaging. Neurobiology of disease. 2018;119:79-87.
  6. van Bergen JMG, Li X, Quevenco FC, Gietl AF, Treyer V, Meyer R, et al. Simultaneous quantitative susceptibility mapping and Flutemetamol-PET suggests local correlation of iron and β-amyloid as an indicator of cognitive performance at high age. NeuroImage. 2018;174:308-16.
  7. Liu J-L, Fan Y-G, Yang Z-S, Wang Z-Y, Guo C. Iron and Alzheimer’s Disease: From Pathogenesis to Therapeutic Implications. Frontiers in neuroscience. 2018;12:632-.
  8. Bilgic B, Pfefferbaum A, Rohlfing T, Sullivan EV, Adalsteinsson E. MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping. NeuroImage. 2012;59(3):2625-35.
  9. Bolt H, Marchan R. Iron dysregulation: an important aspect in toxicology. Archives of toxicology. 2010;84(11):823-4.
  10. Lee J-H, Lee M-S. Brain Iron Accumulation in Atypical Parkinsonian Syndromes: in vivo MRI Evidences for Distinctive Patterns. Frontiers in Neurology. 2019;10.
  11. Syam K. Quantitative estimation of regional brain iron deposition-a potential biomarker for Parkinson’s Disease and other neurodegenerative conditions causing a typical Parkinsonism. SCTIMST; 2021.
  12. Zhao Y, Raichle ME, Wen J, Benzinger TL, Fagan AM, Hassenstab J, et al. In vivo detection of microstructural correlates of brain pathology in preclinical and early Alzheimer Disease with magnetic resonance imaging. NeuroImage. 2017;148:296-304.
  13. Li J, Chang S, Liu T, Wang Q, Cui D, Chen X, et al. Reducing the object orientation dependence of susceptibility effects in gradient echo MRI through quantitative susceptibility mapping. Magnetic resonance in medicine. 2012;68(5):1563-9.
  14. Walsh AJ, Wilman AH. Susceptibility phase imaging with comparison to R2 mapping of iron-rich deep grey matter. NeuroImage. 2011;57(2):452-61.
  15. Du L, Zhao Z, Cui A, Zhu Y, Zhang L, Liu J, et al. Increased Iron Deposition on Brain Quantitative Susceptibility Mapping Correlates with Decreased Cognitive Function in Alzheimer’s Disease. ACS chemical neuroscience. 2018;9(7):1849-57.
  16. Langkammer C, Schweser F, Krebs N, Deistung A, Goessler W, Scheurer E, et al. Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study. NeuroImage. 2012;62(3):1593-9.
  17. Li J, Chang S, Liu T, Wang Q, Cui D, Chen X, et al. Reducing the object orientation dependence of susceptibility effects in gradient echo MRI through quantitative susceptibility mapping. Magnetic resonance in medicine. 2012;68(5):1563-9.
  18. Meadowcroft MD, Connor JR, Smith MB, Yang QX. MRI and histological analysis of beta‐amyloid plaques in both human Alzheimer’s disease and APP/PS1 transgenic mice. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine. 2009;29(5):997-1007.
  19. Wang Y, Liu T. Quantitative susceptibility mapping (QSM): decoding MRI data for a tissue magnetic biomarker. Magnetic resonance in medicine. 2015;73(1):82-101.
  20. Liu T, Spincemaille P, De Rochefort L, Kressler B, Wang Y. Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine. 2009;61(1):196-204.
  21. Hagemeier J, Zivadinov R, Dwyer MG, Polak P, Bergsland N, Weinstock-Guttman B, et al. Changes of deep gray matter magnetic susceptibitwoity over 2 years in multiple sclerosis and healthy control brain. NeuroImage Clinical. 2018;18:1007-16.
  22. Cogswell PM, Wiste HJ, Senjem ML, Gunter JL, Weigand SD, Schwarz CG, et al. Associations of quantitative susceptibility mapping with Alzheimer’s disease clinical and imaging markers. NeuroImage. 2021;224:117433.
  23. Reichenbach J, Schweser F, Serres B, Deistung A. Quantitative susceptibility mapping: concepts and applications. Clinical neuroradiology. 2015;25(2):225-30.
  24. Wen Y, Wang Y, Liu T. Enhancing k‐space quantitative susceptibility mapping by enforcing consistency on the cone data (CCD) with structural priors. Magnetic resonance in medicine. 2016;75(2):823-30.
  25. Sun H, Wilman AH. Background field removal using spherical mean value filtering and Tikhonov regularization. Magnetic resonance in medicine. 2014;71(3):1151-7.
  26. Au CKF, Abrigo J, Liu C, Liu W, Lee J, Au LWC, et al. Quantitative Susceptibility Mapping of the Hippocampal Fimbria in Alzheimer’s Disease. Journal of Magnetic Resonance Imaging. 2021;53(6):1823-32.
  27. Nikparast F, Ganji Z, Danesh Doust M, Faraji R, Zare H. Brain pathological changes during neurodegenerative diseases and their identification methods: How does QSM perform in detecting this process? Insights into Imaging. 2022;13(1):74.
  28. Nikparast F, Ganji Z, Zare H. Early differentiation of neurodegenerative diseases using the novel QSM technique: what is the biomarker of each disorder? BMC Neurosci. 2022;23(1):48.
  29. Straub S, Schneider TM, Emmerich J, Freitag MT, Ziener CH, Schlemmer HP, et al. Suitable reference tissues for quantitative susceptibility mapping of the brain. Magnetic resonance in medicine. 2017;78(1):204-14.
  30. Li W, Wu B, Liu C. Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition. NeuroImage. 2011;55(4):1645-56.
  31. Wei H, Dibb R, Zhou Y, Sun Y, Xu J, Wang N, et al. Streaking artifact reduction for quantitative susceptibility mapping of sources with large dynamic range. NMR in biomedicine. 2015;28(10):1294-303.
  32. Li W, Wu B, Batrachenko A, Bancroft‐Wu V, Morey RA, Shashi V, et al. Differential developmental trajectories of magnetic susceptibility in human brain gray and white matter over the lifespan. Human brain mapping. 2014;35(6):2698-713.
  33. Feng X, Deistung A, Reichenbach JR. Quantitative susceptibility mapping (QSM) and R2* in the human brain at 3 T: Evaluation of intra-scanner repeatability. Zeitschrift für Medizinische Physik. 2018;28(1):36-48.
  34. Chan K-S, Marques JP. SEPIA—susceptibility mapping pipeline tool for phase images. NeuroImage. 2021;227:117611.
  35. Patenaude B, Smith SM, Kennedy DN, Jenkinson M. A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage. 2011;56(3):907-22.
  36. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magnetic resonance imaging. 2012;30(9):1323-41.
  37. Ayton S, Wang Y, Diouf I, Schneider JA, Brockman J, Morris MC, et al. Brain iron is associated with accelerated cognitive decline in people with Alzheimer pathology. Molecular psychiatry. 2020;25(11):2932-41.
  38. Galante D, Cavallo E, Perico A, D’Arrigo C. Effect of ferric citrate on amyloid‐beta peptides behavior. Biopolymers. 2018;109(6):e23224.
  39. Tahmasebinia F, Emadi S. Effect of metal chelators on the aggregation of beta-amyloid peptides in the presence of copper and iron. Biometals. 2017;30(2):285-93.
  40. Hwang EJ, Kim HG, Kim D, Rhee HY, Ryu CW, Liu T, et al. Texture analyses of quantitative susceptibility maps to differentiate Alzheimer’s disease from cognitive normal and mild cognitive impairment. Medical physics. 2016;43(8):4718.
  41. Tiepolt S, Schäfer A, Rullmann M, Roggenhofer E, Gertz HJ, Schroeter ML, et al. Quantitative Susceptibility Mapping of Amyloid-β Aggregates in Alzheimer’s Disease with 7T MR. Journal of Alzheimer’s disease : JAD. 2018;64(2):393-404.
  42. Peters DG, Connor JR, Meadowcroft MD. The relationship between iron dyshomeostasis and amyloidogenesis in Alzheimer’s disease: two sides of the same coin. Neurobiology of disease. 2015;81:49-65.
  43. Zidan M, Boban J, Bjelan M, Todorović A, Stankov Vujanić T, Semnic M, et al. Thalamic volume loss as an early sign of amnestic mild cognitive impairment. Journal of Clinical Neuroscience. 2019;68:168-73.
  44. Acosta-Cabronero J, Williams GB, Cardenas-Blanco A, Arnold RJ, Lupson V, Nestor PJ. In vivo quantitative susceptibility mapping (QSM) in Alzheimer’s disease. PloS one. 2013;8(11):e81093.
  45. Kim HG, Park S, Rhee HY, Lee KM, Ryu CW, Rhee SJ, et al. Quantitative susceptibility mapping to evaluate the early stage of Alzheimer’s disease. NeuroImage Clinical. 2017;16:429-38.
  46. Kan H, Uchida Y, Arai N, Ueki Y, Aoki T, Kasai H, et al. Simultaneous voxel-based magnetic susceptibility and morphometry analysis using magnetization-prepared spoiled turbo multiple gradient echo. NMR in biomedicine. 2020;33(5):e4272.
  47. Li D, Liu Y, Zeng X, Xiong Z, Yao Y, Liang D, et al. Quantitative Study of the Changes in Cerebral Blood Flow and Iron Deposition During Progression of Alzheimer’s Disease. Journal of Alzheimer’s disease : JAD. 2020;78(1):439-52.

 

 

 

 

 

Iranian Journal of Medical Physics
Volume 21, Issue 1 - Serial Number 1
January and February 2024
Pages 45-52

Files

History

  • Receive Date: 15 October 2022
  • Revise Date: 19 December 2022
  • Accept Date: 25 December 2022

Share

How to cite

Statistics