Skip to main content Skip to main navigation menu Skip to site footer

Journal of Ideas in Health

Articles
DOI: 10.47108/jidhealth.Vol8.Iss4.422
Submitted: 2025-07-06
Published: 2025-08-31

Visual rating of GCA and MTA scoring with deep white matter hyperintensities in relation to age and cognitive value:

Department of Radiology, Baquba Teaching Hospital, Diyala Health Directorate, 32001, Diyala, Iraq
Department of Radiologist Specialist, Baghdad Medical City Complex, Baghdad Teaching Hospital, Baghdad, Iraq
Magnetic Resonance Imaging, Cognitive Disorders, Visual Rating Scales, White Matter Hyperintensities, Iraq

Abstract

Background: The cognitive dysfunction disorders are nowadays represented great health, social, and economic burden globally. Magnetic resonance imaging plays a major role in the evaluation of these disorders. This study aims to assess the relationship between the visual rating scale of global cerebral atrophy (GCA) & medial temporal lobe atrophy (MTA) scoring with age, white matter hyper-intensities, and cognitive value.

Methods: A cross-sectional study carried out from 1st of November 2022 to 28th of February 2023 at the Magnetic Resonance Imaging unit of the Radiology department in Baghdad Teaching Hospital at Medical Complex in Baghdad city, Iraq. Sixty patients aged over 45 years with suspected cognitive abnormalities were included, while younger patients, those with territorial infarction, watershed infarction, or unwillingness to participate were excluded. Data were collected through a semi-structured questionnaire covering sociodemographic factors, chronic illnesses, cognitive status, MRI findings, and visual rating scales (Fazekas, Global Cortical Atrophy [GCA], and Medial Temporal Lobe Atrophy [MTA]). MRI was performed using a 1.5T Philips Achieva Nova scanner, and cognitive assessment was conducted with the Mini-Mental State Examination (MMSE).

Results: The mean age of participants was 62.4 ± 8.8 years, with males slightly predominating (55%). Chronic diseases were common, particularly hypertension (57.2%). Cognitive assessment revealed 51.7% with normal cognition, 18.3% with mild cognitive impairment, and 30% with dementia. Higher Fazekas, GCA, and MTA scores were significantly associated with dementia (p<0.05). MTA scores were significantly elevated in Alzheimer’s disease (p<0.001), while vascular etiologies were strongly associated with higher GCA, Fazekas, and MTA scores (p<0.05). Non-strategic lacunar ischemia showed higher Fazekas scores compared to strategic types (p=0.006). Increasing age was significantly linked to dementia, vascular pathology, and higher atrophy scores.

Conclusion: These findings suggest a strong correlation between structural brain changes observed on MRI and cognitive decline, highlighting the importance of visual rating scales in clinical assessment.



Downloads

Download data is not yet available.

References

  1. Qi X, Tang H, Luo Q, Ding B, Chen J, Cui P, Chen S, Ling H, Ma J. White Matter Hyperintensities Predict Cognitive Decline: A Community-Based Study. Can J Neurol Sci. 2019 Jul;46(4):383-388. doi: 10.1017/cjn.2019.47.
  2. Wang Y, Yang Y, Wang T, Nie S, Yin H, Liu J. Correlation between White Matter Hyperintensities Related Gray Matter Volume and Cognition in Cerebral Small Vessel Disease. J Stroke Cerebrovasc Dis. 2020 Dec;29(12):105275. doi: 10.1016/j.jstrokecerebrovasdis.2020.105275.
  3. Brugulat-Serrat A, Salvadó G, Operto G, Cacciaglia R, Sudre CH, Grau-Rivera O, et al; ALFA Study. White matter hyperintensities mediate gray matter volume and processing speed relationship in cognitively unimpaired participants. Hum Brain Mapp. 2020 Apr 1;41(5):1309-1322. doi: 10.1002/hbm.24877.
  4. d'Arbeloff T, Elliott ML, Knodt AR, Melzer TR, Keenan R, Ireland D, et al. White matter hyperintensities are common in midlife and already associated with cognitive decline. Brain Commun. 2019;1(1):fcz041. doi: 10.1093/braincomms/fcz041.
  5. Valdés Hernández MDC, Chappell FM, Muñoz Maniega S, Dickie DA, Royle NA, Morris Z, et al. Metric to quantify white matter damage on brain magnetic resonance images. Neuroradiology. 2017 Oct;59(10):951-962. doi: 10.1007/s00234-017-1892-1.
  6. Murray ME, Vemuri P, Preboske GM, Murphy MC, Schweitzer KJ, Parisi JE, Jack CR Jr, Dickson DW. A quantitative postmortem MRI design sensitive to white matter hyperintensity differences and their relationship with underlying pathology. J Neuropathol Exp Neurol. 2012 Dec;71(12):1113-22. doi: 10.1097/NEN.0b013e318277387e.
  7. Al-Janabi OM, Bauer CE, Goldstein LB, Murphy RR, Bahrani AA, Smith CD, Wilcock DM, Gold BT, Jicha GA. White Matter Hyperintensity Regression: Comparison of Brain Atrophy and Cognitive Profiles with Progression and Stable Groups. Brain Sci. 2019 Jul 19;9(7):170. doi: 10.3390/brainsci9070170.
  8. Ramirez J, McNeely AA, Berezuk C, Gao F, Black SE. Dynamic Progression of White Matter Hyperintensities in Alzheimer's Disease and Normal Aging: Results from the Sunnybrook Dementia Study. Front Aging Neurosci. 2016 Mar 24;8:62. doi: 10.3389/fnagi.2016.00062.
  9. Appelman AP, Exalto LG, van der Graaf Y, Biessels GJ, Mali WP, Geerlings MI. White matter lesions and brain atrophy: more than shared risk factors? A systematic review. Cerebrovasc Dis. 2009;28(3):227-42. doi: 10.1159/000226774.
  10. Schmidt R, Ropele S, Enzinger C, Petrovic K, Smith S, Schmidt H, Matthews PM, Fazekas F. White matter lesion progression, brain atrophy, and cognitive decline: the Austrian stroke prevention study. Ann Neurol. 2005 Oct;58(4):610-6. doi: 10.1002/ana.20630
  11. Faldu H, Surana D, Patel C. Clinical spectrum of demyelinating disease of central nervous system and frequency of anti AQP4 and anti MOG among them: one-year single-center retrospective study. J Ideas Health. 2024 Aug. 31 ;7(4):1100-5. DOI: 10.47108/jidhealth.Vol7.Iss4.353
  12. Gattringer T, Enzinger C, Ropele S, Gorani F, Petrovic KE, Schmidt R, Fazekas F. Vascular risk factors, white matter hyperintensities and hippocampal volume in normal elderly individuals. Dement Geriatr Cogn Disord. 2012;33(1):29-34. doi: 10.1159/000336052.
  13. Park M, Moon WJ. Structural MR Imaging in the Diagnosis of Alzheimer's Disease and Other Neurodegenerative Dementia: Current Imaging Approach and Future Perspectives. Korean J Radiol. 2016 Nov-Dec;17(6):827-845. doi: 10.3348/kjr.2016.17.6.827.
  14. Resnick SM, Goldszal AF, Davatzikos C, Golski S, Kraut MA, Metter EJ, Bryan RN, Zonderman AB. One-year age changes in MRI brain volumes in older adults. Cereb Cortex. 2000 May;10(5):464-72. doi: 10.1093/cercor/10.5.464.
  15. Hua X, Ching CRK, Mezher A, Gutman BA, Hibar DP, Bhatt P, Leow AD, Jack CR Jr, Bernstein MA, Weiner MW, Thompson PM; Alzheimer's Disease Neuroimaging Initiative. MRI-based brain atrophy rates in ADNI phase 2: acceleration and enrichment considerations for clinical trials. Neurobiol Aging. 2016 Jan;37:26-37. doi: 10.1016/j.neurobiolaging.2015.09.018.
  16. Vernooij MW, van Buchem MA. Neuroimaging in Dementia. 2020 Feb 15. In: Hodler J, Kubik-Huch RA, von Schulthess GK, editors. Diseases of the Brain, Head and Neck, Spine 2020–2023: Diagnostic Imaging [Internet]. Cham (CH): Springer; 2020. Chapter 11. Available from: https://www.ncbi.nlm.nih.gov/books/NBK554327/
  17. Pereira JB, Cavallin L, Spulber G, Aguilar C, Mecocci P, Vellas B, Tsolaki M, Kłoszewska I, Soininen H, Spenger C, Aarsland D, Lovestone S, Simmons A, Wahlund LO, Westman E; AddNeuroMed consortium and for the Alzheimer's Disease Neuroimaging Initiative. Influence of age, disease onset and ApoE4 on visual medial temporal lobe atrophy cut-offs. J Intern Med. 2014 Mar;275(3):317-30. doi: 10.1111/joim.12148.
  18. Aribisala BS, Valdés Hernández MC, Royle NA, Morris Z, Muñoz Maniega S, Bastin ME, Deary IJ, Wardlaw JM. Brain atrophy associations with white matter lesions in the ageing brain: the Lothian Birth Cohort 1936. Eur Radiol. 2013 Apr;23(4):1084-92. doi: 10.1007/s00330-012-2677-x.
  19. Zheng L, Vinters HV, Mack WJ, Weiner MW, Chui HC; IVD program project. Differential effects of ischemic vascular disease and Alzheimer's disease on brain atrophy and cognition. J Cereb Blood Flow Metab. 2016 Jan;36(1):204-15. doi: 10.1038/jcbfm.2015.152.
  20. Prins ND, Scheltens P. White matter hyperintensities, cognitive impairment and dementia: an update. Nat Rev Neurol. 2015 Mar;11(3):157-65. doi: 10.1038/nrneurol.2015.10.
  21. Østergaard L, Engedal TS, Moreton F, Hansen MB, Wardlaw JM, Dalkara T, Markus HS, Muir KW. Cerebral small vessel disease: Capillary pathways to stroke and cognitive decline. J Cereb Blood Flow Metab. 2016 Feb;36(2):302-25. doi: 10.1177/0271678X15606723.
  22. Sachdev PS, Blacker D, Blazer DG, Ganguli M, Jeste DV, Paulsen JS, Petersen RC. Classifying neurocognitive disorders: the DSM-5 approach. Nat Rev Neurol. 2014 Nov;10(11):634-42. doi: 10.1038/nrneurol.2014.181.
  23. Reitz C, Mayeux R. Alzheimer disease: epidemiology, diagnostic criteria, risk factors and biomarkers. Biochem Pharmacol. 2014 Apr 15;88(4):640-51. doi: 10.1016/j.bcp.2013.12.024.
  24. O'Brien JT, Thomas A. Vascular dementia. Lancet. 2015 Oct 24;386(10004):1698-706. doi: 10.1016/S0140-6736(15)00463-8.
  25. Custodio N, Montesinos R, Lira D, Herrera-Pérez E, Bardales Y, Valeriano-Lorenzo L. Mixed dementia: A review of the evidence. Dement Neuropsychol. 2017 Oct-Dec;11(4):364-370. doi: 10.1590/1980-57642016dn11-040005.
  26. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011 May;7(3):263-9. doi: 10.1016/j.jalz.2011.03.005.
  27. Filley CM, Fields RD. White matter and cognition: making the connection. J Neurophysiol. 2016 Nov 1;116(5):2093-2104. doi: 10.1152/jn.00221.2016.
  28. Muzio MR, Cascella M. StatPearls [Internet]. StatPearls Publishing; Treasure Island (FL): Nov 19, 2021. Histology, Axon.
  29. Nave KA, Werner HB. Myelination of the nervous system: mechanisms and functions. Annu Rev Cell Dev Biol. 2014;30:503-33. doi: 10.1146/annurev-cellbio-100913-013101.
  30. Habes M, Sotiras A, Erus G, Toledo JB, Janowitz D, Wolk DA, et al. White matter lesions: Spatial heterogeneity, links to risk factors, cognition, genetics, and atrophy. Neurology. 2018 Sep 4;91(10):e964-e975. doi: 10.1212/WNL.0000000000006116.
  31. Smith CD, Johnson ES, Van Eldik LJ, Jicha GA, Schmitt FA, Nelson PT, et al. Peripheral (deep) but not periventricular MRI white matter hyperintensities are increased in clinical vascular dementia compared to Alzheimer's disease. Brain Behav. 2016 Feb 16;6(3):e00438. doi: 10.1002/brb3.438.
  32. Sotiras A, Resnick SM, Davatzikos C. Finding imaging patterns of structural covariance via Non-Negative Matrix Factorization. Neuroimage. 2015 Mar;108:1-16. doi: 10.1016/j.neuroimage.2014.11.045.
  33. Blanco-Rojas L, Arboix A, Canovas D, Grau-Olivares M, Oliva Morera JC, Parra O. Cognitive profile in patients with a first-ever lacunar infarct with and without silent lacunes: a comparative study. BMC Neurol. 2013 Dec 16;13:203. doi: 10.1186/1471-2377-13-203.
  34. Wardlaw JM, Smith C, Dichgans M. Small vessel disease: mechanisms and clinical implications. Lancet Neurol. 2019 Jul;18(7):684-696. doi: 10.1016/S1474-4422(19)30079-1.
  35. Nelson RF, Pullicino P, Kendall BE, Marshall J. Computed tomography in patients presenting with lacunar syndromes. Stroke. 1980 May-Jun;11(3):256-61. doi: 10.1161/01.str.11.3.256.
  36. Noguchi K, Nagayoshi T, Watanabe N, Kanazawa T, Toyoshima S, Morijiri M, Shojaku H, Shimizu M, Seto H. Diffusion-weighted echo-planar MRI of lacunar infarcts. Neuroradiology. 1998 Jul;40(7):448-51. doi: 10.1007/s002340050621.
  37. Schonewille WJ, Tuhrim S, Singer MB, Atlas SW. Diffusion-weighted MRI in acute lacunar syndromes. A clinical-radiological correlation study. Stroke. 1999 Oct;30(10):2066-9. doi: 10.1161/01.str.30.10.2066.
  38. Tan MY, Singhal S, Ma H, Chandra RV, Cheong J, Clissold BB, Ly J, Srikanth V, Phan TG. Examining Subcortical Infarcts in the Era of Acute Multimodality CT Imaging. Front Neurol. 2016 Dec 5;7:220. doi: 10.3389/fneur.2016.00220.
  39. Zhao L, Biesbroek JM, Shi L, Liu W, Kuijf HJ, Chu WW, Abrigo JM, Lee RK, Leung TW, Lau AY, Biessels GJ, Mok V, Wong A. Strategic infarct location for post-stroke cognitive impairment: A multivariate lesion-symptom mapping study. J Cereb Blood Flow Metab. 2018 Aug;38(8):1299-1311. doi: 10.1177/0271678X17728162.
  40. El-Hayek YH, Wiley RE, Khoury CP, Daya RP, Ballard C, Evans AR, Karran M, Molinuevo JL, Norton M, Atri A. Tip of the Iceberg: Assessing the Global Socioeconomic Costs of Alzheimer's Disease and Related Dementias and Strategic Implications for Stakeholders. J Alzheimers Dis. 2019;70(2):323-341. doi: 10.3233/JAD-190426.
  41. Han EJ, Lee J, Cho E, Kim H. Socioeconomic Costs of Dementia Based on Utilization of Health Care and Long-Term-Care Services: A Retrospective Cohort Study. Int J Environ Res Public Health. 2021 Jan 6;18(2):376. doi: 10.3390/ijerph18020376.
  42. Doubal FN, MacLullich AM, Ferguson KJ, Dennis MS, Wardlaw JM. Enlarged perivascular spaces on MRI are a feature of cerebral small vessel disease. Stroke. 2010 Mar;41(3):450-4. doi: 10.1161/STROKEAHA.
  43. Zdanovskis N, Platkājis A, Kostiks A, Šneidere K, Stepens A, Naglis R, Karelis G. Combined Score of Perivascular Space Dilatation and White Matter Hyperintensities in Patients with Normal Cognition, Mild Cognitive Impairment, and Dementia. Medicina (Kaunas). 2022 Jul 1;58(7):887. doi: 10.3390/medicina58070887.
  44. El-Metwally A, Toivola P, Al-Rashidi M, Nooruddin S, Jawed M, AlKanhal R, Razzak HA, Albawardi N. Epidemiology of Alzheimer's Disease and Dementia in Arab Countries: A Systematic Review. Behav Neurol. 2019 Oct 29;2019:3935943. doi: 10.1155/2019/3935943.
  45. Wolters FJ, Chibnik LB, Waziry R, Anderson R, Berr C, Beiser A, et al. Twenty-seven-year time trends in dementia incidence in Europe and the United States: The Alzheimer Cohorts Consortium. Neurology. 2020 Aug 4;95(5):e519-e531. doi: 10.1212/WNL.0000000000010022.
  46. Stephan Y, Sutin AR, Luchetti M, Terracciano A. Subjective age and risk of incident dementia: Evidence from the National Health and Aging Trends survey. J Psychiatr Res. 2018 May;100:1-4. doi: 10.1016/j.jpsychires.2018.02.008.
  47. Muñoz Maniega S, Chappell FM, Valdés Hernández MC, Armitage PA, Makin SD, Heye AK, Thrippleton MJ, Sakka E, Shuler K, Dennis MS, Wardlaw JM. Integrity of normal-appearing white matter: Influence of age, visible lesion burden and hypertension in patients with small-vessel disease. J Cereb Blood Flow Metab. 2017 Feb;37(2):644-656. doi: 10.1177/0271678X16635657.
  48. Xiong YY, Mok V. Age-related white matter changes. J Aging Res 2011; 2011:617927.
  49. Al-Janabi OM, Panuganti P, Abner EL, Bahrani AA, Murphy R, Bardach SH, Caban-Holt A, Nelson PT, Gold BT, Smith CD, Wilcock DM, Jicha GA. Global Cerebral Atrophy Detected by Routine Imaging: Relationship with Age, Hippocampal Atrophy, and White Matter Hyperintensities. J Neuroimaging. 2018 May;28(3):301-306. doi: 10.1111/jon.
  50. Blinkouskaya Y, Weickenmeier J. Brain Shape Changes Associated With Cerebral Atrophy in Healthy Aging and Alzheimer's Disease. Front Mech Eng 2021; 7:705653.
  51. Claus JJ, Staekenborg SS, Holl DC, Roorda JJ, Schuur J, Koster P, Tielkes CEM, Scheltens P. Practical use of visual medial temporal lobe atrophy cut-off scores in Alzheimer's disease: Validation in a large memory clinic population. Eur Radiol. 2017 Aug;27(8):3147-3155. doi: 10.1007/s00330-016-4726-3.
  52. Arboix A, García-Eroles L, Massons J, Oliveres M, Targa C. Lacunar infarcts in patients aged 85 years and older. Acta Neurol Scand. 2000 Jan;101(1):25-9. doi: 10.1034/j.1600-0404.2000.00005.x.
  53. Cai Z, He W, Peng CY, Zhou J, Xu QL, Wu ZS. The prevalence of lacunar infarct decreases with aging in the elderly: a case-controlled analysis. Clin Interv Aging. 2016 May 26;11:733-8. doi: 10.2147/CIA.S108166.
  54. Yu L, Yang L, Zhang X, Yuan J, Li Y, Yang S, Gu H, Hu W, Gao S. Age and recurrent stroke are related to the severity of white matter hyperintensities in lacunar infarction patients with diabetes. Clin Interv Aging. 2018 Dec 7;13:2487-2494. doi: 10.2147/CIA.S184463.
  55. Farfel JM, Yu L, Boyle PA, Leurgans S, Shah RC, Schneider JA, Bennett DA. Alzheimer's disease frequency peaks in the tenth decade and is lower afterwards. Acta Neuropathol Commun. 2019 Jul 3;7(1):104. doi: 10.1186/s40478-019-0752-0.
  56. Kaushik S, Vani K, Chumber S, Anand KS, Dhamija RK. Evaluation of MR Visual Rating Scales in Major Forms of Dementia. J Neurosci Rural Pract. 2021 Jan;12(1):16-23. doi: 10.1055/s-0040-1716806.
  57. Mårtensson G, Ferreira D, Cavallin L, Muehlboeck JS, Wahlund LO, Wang C, Westman E; Alzheimer's Disease Neuroimaging Initiative. AVRA: Automatic visual ratings of atrophy from MRI images using recurrent convolutional neural networks. Neuroimage Clin. 2019;23:101872. doi: 10.1016/j.nicl.2019.101872.
  58. Molinder A, Ziegelitz D, Maier SE. Validity and reliability of the medial temporal lobe atrophy scale in a memory clinic population. BMC Neurol 2021; 21: 289. Available from: https://doi.org/10.1186/s12883-021-02325-2.
  59. Wahlund LO, Westman E, van Westen D, Wallin A, Shams S, Cavallin L, Larsson EM; From the Imaging Cognitive Impairment Network (ICINET). Imaging biomarkers of dementia: recommended visual rating scales with teaching cases. Insights Imaging. 2017 Feb;8(1):79-90. doi: 10.1007/s13244-016-0521-6.
  60. Agarwal P, Panda AK, Jena S, Mohapatra S. Correlation of Cerebral Atrophy and White Matter Hyperintensity Burden in MRI with Clinical Cognitive Decline. Siriraj Med J 2022; 74(5):323-329. Available from: https://he02.tci-
  61. Visser PJ, Verhey FR, Hofman PA, Scheltens P, Jolles J. Medial temporal lobe atrophy predicts Alzheimer's disease in patients with minor cognitive impairment. J Neurol Neurosurg Psychiatry. 2002 Apr;72(4):491-7. doi: 10.1136/jnnp.72.4.491.
  62. Kaltoft NS, Marner L, Larsen VA, Hasselbalch SG, Law I, Henriksen OM. Hybrid FDG PET/MRI vs. FDG PET and CT in patients with suspected dementia - A comparison of diagnostic yield and propagated influence on clinical diagnosis and patient management. PLoS One. 2019 May 2;14(5):e0216409..
  63. enjamin P, Trippier S, Lawrence AJ, Lambert C, Zeestraten E, Williams OA, Patel B, Morris RG, Barrick TR, MacKinnon AD, Markus HS. Lacunar Infarcts, but Not Perivascular Spaces, Are Predictors of Cognitive Decline in Cerebral Small-Vessel Disease. Stroke. 2018 Mar;49(3):586-593. doi: 10.1161/STROKEAHA.117.017526.


How to Cite

1.
Salman R, Hassan F. Visual rating of GCA and MTA scoring with deep white matter hyperintensities in relation to age and cognitive value: . J Ideas Health [Internet]. 2025 Aug. 31 [cited 2025 Sep. 21];8(4):1313-21. Available from: https://jidhealth.com/index.php/jidhealth/article/view/422

issn

Journal of Ideas in Health

Four Issues Annually

www.jidhealth.com

eISSN: 2645-9248

Founded: 2017

call

Make a Submission

Make a Submission

ToBeRev

road

google

pkpindex

oai

base

tools



cc

doaj

mendeley

doi

jisc

Keywords