Diagnosis of Mild Cognitive Impairment (MCI) via Estimating the Density of Gray Matter Using Voxel-Based Morphometry (VBM) in the Brain Magnetic Resonance Imaging (MRI)

Document Type : Original Article (s)

Authors

1 MSc Student, Department of Biomedical Engineering, School of Advanced Medical Technology AND Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran

2 Assistant Professor, Department of Physics and Medical Engineering, School of Medicine AND Department of Biomedical Engineering, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran

3 Professor, Psychosomatic Research Center AND Department of Psychiatry, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

Background: Mild cognitive impairment (MCI) is a transmition phase between the normal elderly and demance which is due to the loss of brain cells, tends to irreversible loss of mental abilities and should be predicted at early stage. Common methods of diagnosis are biochemical and psychological tests. Recently, ragrding the developments in analysis of magnetic resonance imaging (MRI), it has been attended as a noninvasive and low-cost method. In this study, we tried to diagnos mild cognitive impairment via estimating the volume of gray matter using voxel-based morphometry (VBM) in brain MRI.Methods: 20 patients with mild cognitive impairment and 20 normal subjects aged 66.4 ± 4.6 and 65.3 ± 3.9 years, respectively, were assessed. All subjects underwent neuropsychological testing using Neuropsychiatry Unit Cognitive assessment tool and were scanned with 1.5T MRI. Images were pre-processed and analyzed using SPM8 running on Matlab2013b software. Finally for statistical analyses, modulated images of two groups were compared.Findings: Patients with mild cognitive impairment had significantly lower brain gray matter density in superior frontal gyrus (P = 0.013), inferior temporal gyrus (P = 0.013), inferior frontal gyrus (P = 0.017), paracentral lobule, superior temporal gyru, paracentral lobule (P = 0.022), and insula and middle temporal gyrus (P = 0.030).Conclusion: In this study were found several regions of local brain atrophy in patients with mild cognitive impairment within the regions known to be involved in early brain atrophy in mild cognitive impairment that reduce gray matter density.

Keywords

References

  1. Pennanen C, Kivipelto M, Tuomainen S, Hartikainen P, Hanninen T, Laakso MP, et al. Hippocampus and entorhinal cortex in mild cognitive impairment and early AD. Neurobiol Aging 2004; 25(3): 303-10.
  2. Barekatain M, Tavakoli M. Neuropsychological assessments in early diagnosis of dementia. J ResBehav Sci 2012; 10(3): 228-38. [In Persian].
  3. Mirzaei M, Shams Ghahfarokhi M. Demography of eldery in Iran according to the census 1956-2006. Salmand Iran J Ageing 2007; 2(5): 326-31. [In Persian].
  4. Manly JJ, Bell-McGinty S, Tang MX, Schupf N, Stern Y, Mayeux R. Implementing diagnostic criteria and estimating frequency of mild cognitive impairment in an urban community. Arch Neurol 2005; 62(11): 1739-46.
  5. Kermani S, Karimi Z, Barekatain M. Evaluation and estimation of gray matter volume using voxel-based morphometry of the brain magnetic resonance imaging (MRI) in normal elderly people and those with mild cognitive impairment. J Isfahan Med Sch 2015; 33(353): 1649-61.
  6. Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004; 256(3): 183-94.
  7. Bickel H, Mosch E, Seigerschmidt E, Siemen M, Forstl H. Prevalence and persistence of mild cognitive impairment among elderly patients in general hospitals. Dement Geriatr Cogn Disord 2006; 21(4): 242-50.
  8. Whitwell JL, Przybelski SA, Weigand SD, Knopman DS, Boeve BF, Petersen RC, et al. 3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer's disease. Brain 2007; 130(Pt 7): 1777-86.
  9. Barekatain M, Askarpour H, Zahedian F, Walterfang M, Velakoulis D, Maracy MR, et al. The relationship between regional brain volumes and the extent of coronary artery disease in mild cognitive impairment. J Res Med Sci 2014; 19(8): 739-45.
  10. Mosconi L, De SS, Li J, Tsui WH, Li Y, Boppana M, et al. Hippocampal hypometabolism predicts cognitive decline from normal aging. Neurobiol Aging 2008; 29(5): 676-92.
  11. Whitwell JL, Petersen RC, Negash S, Weigand SD, Kantarci K, Ivnik RJ, et al. Patterns of atrophy differ among specific subtypes of mild cognitive impairment. Arch Neurol 2007; 64(8): 1130-8.
  12. Risacher SL, Saykin AJ, West JD, Shen L, Firpi HA, McDonald BC. Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Curr Alzheimer Res 2009; 6(4): 347-61.
  13. Karas GB, Scheltens P, Rombouts SA, Visser PJ, van Schijndel RA, Fox NC, et al. Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease. Neuroimage 2004; 23(2): 708-16.
  14. Luck T, Luppa M, Briel S, Riedel-Heller SG. Incidence of mild cognitive impairment: a systematic review. Dement Geriatr Cogn Disord 2010; 29(2): 164-75.
  15. Resnick SM, Pham DL, Kraut MA, Zonderman AB, Davatzikos C. Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J Neurosci 2003; 23(8): 3295-301.
  16. Whitwell JL, Petersen RC, Negash S, Weigand SD, Kantarci K, Ivnik RJ, et al. Patterns of atrophy differ among specific subtypes of mild cognitive impairment. Arch Neurol 2007; 64(8): 1130-8.
  17. Chetelat G, Landeau B, Eustache F, Mezenge F, Viader F, de lS, V, et al. Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study. Neuroimage 2005; 27(4): 934-46.
  18. Fan Y, Batmanghelich N, Clark CM, Davatzikos C. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 2008; 39(4): 1731-43.
  19. Ferreira LK, Diniz BS, Forlenza OV, Busatto GF, Zanetti MV. Neurostructural predictors of Alzheimer's disease: a meta-analysis of VBM studies. Neurobiol Aging 2011; 32(10): 1733-41.
  20. Kovacevic S, Rafii MS, Brewer JB. High-throughput, fully-automated volumetry for prediction of MMSE and CDR decline in mild cognitive impairment. Alzheimer Dis Assoc Disord 2009; 23(2): 139-45.
Journal of Isfahan Medical School
Volume 33, Issue 363 - Serial Number 363
November and December 2016
Pages 2204-2212

Files

History

  • Receive Date: 15 March 2015
  • Revise Date: 02 November 2024
  • Accept Date: 06 April 2022

Share

How to cite

Statistics