THE ROLE OF METHODS FOR QUANTITATIVE ANALYSIS OF MAGNETIC RESONANCE IMAGING DATA IN THE DIAGNOSIS OF ALZHEIMER'S DISEASE AT AN EARLY STAGE

Alzheimer's disease (AD) is leading in the prevalence in the structure of neurodegenerative diseases and is the most common cause of dementia in the population. It is advisable to conduct therapy at an early stage of the disease, since at the terminal stage of the disease treatment becomes ineffective. In the present situation, timely and accurate diagnostics of AD on early stages of the disease becomes most important. One of the key places in diagnostics of this disease is assigned to neuroimaging methods, in particular, magnetic resonance imaging (MRI). In the submitted systematic review, a search was carried out in electronic databases and scientific electronic libraries Cyberleninka, PubMed, OVID, the Cochrane Collaboration database. The modern aspects of early diagnosis of AD using MRI are assessed. The modern aspects of early diagnostics of AD using MRI are assessed. A number of Russian and foreign articles and metaanalyses devoted to the quantitative evaluation of MR-tomography data at an early stage of AD were analyzed and highlighted.

1. Всемирная организация здравоохранения и Международная организация по проблемам болезни Альцгеймера. Деменция: приоритет общественного здравоохранения. 2012. http://www.who.int/mental_health/ publications/de mentia_report_ 2012/en/ [The World Health Organization and the International Alzheimer's Disease Organization. Dementia: public health priority. 2012. Available at: http://www.who.int/mental_health/ publications/de mentia_report_2012 / en / (in Russ.).]

2. Яхно Н.Н., Захаров В.В., Локшина А.Б., Коберская Н.Н., Мхитарян Э.А. Деменции: Руководство для врачей. 3-е изд. М.: МЕДпресс-информ; 2011. [Yakhno N.N., Zakharov V.V., Lokshina A.B., Koberskaya N.N., Mkhitaryan E.A. Dementia: A guide for doctors. 3rd ed. Moscow: MEDpress-inform; 2011 (in Russ.).]

3. Емелин А.Ю., Одинак М.М., Лобзин В.Ю., Воробьев С.В., Киселев В.Н. Современные возможности нейровизуализации в дифференциальной диагностике когнитивных нарушений. Неврология, нейропсихиатрия, психосоматика. 2012; 4 (2S): 51–5. [Emelin A.Yu., Odinak M.M., Lobzin V.Yu., Vorob’ev S.V., Kiselev V.N. Modern possibilities of neuroimaging in differential diagnosis of cognitive impairment. Nevrologiya, Neyropsikhiatriya, Psikhosomatika (Neurology, Neuropsychiatry, Psychosomatics, Russian journal). 2012; 4 (2S): 51–5 (in Russ.).]

4. Schmitter D., Roche A., Maréchal B., Ribes D., Abdulkadir A., BachCuadra M. et al. An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease. Neuroimage Clin. 2015; 7: 7–17.

5. Hyon-Ah Yi, Möller Ch., Dieleman N., Bouwman F.H., Barkhof F., Scheltens Ph. et al. Relation between subcortical grey matter atrophy and conversion from mild cognitive impairment to Alzheimer’s disease. Neurol. Neurosurg. Psychiatry. 2016; 87: 425–32.

6. Johnson K.A., Fox N.C., Klunk W.E., Reisa A. Sperling brain imaging in Alzheimer disease. Cold. Spring. Harb. Perspect. Med. 2012; 2 (4).

7. Незнанов Н.Г., Ананьева Н.И., Залуцкая Н.М., Стулов И.К., Гальсман И.Е., Бельцева Ю.А. Визуальная шкальная МРТ-оценка атрофических изменений головного мозга в диагностике ранней стадии болезни Альцгеймера (1 этап исследования). Обозрение психиатрии и медицинской психологии. 2016; 4: 61–5. [Neznanov N.G., Anan’eva N.I., Zalutskaya N.M., Stulov I.K., Gal’sman I.E., Bel’tseva Yu.A. Visual scale MRI assessment of atrophic changes in the brain in the diagnosis of early stage Alzheimer's disease (Phase 1 study). Obozrenie Psikhiatrii i Meditsinskoy Psikhologii (Review of Psychiatry and Medical Psychology, Russian journal). 2016; 4: 61–5 (in Russ.).]

8. Магонов Е.П., Катаева Г.В., Трофимова Т.Н. Современные методы автоматического вычисления внутричерепного пространства с помощью МР-морфометрии. Вестник Новгородского государственного университета. 2015; 2 (85). [Magonov E.P., Kataeva G.V., Trofimova T.N. Modern methods of automatic calculation of intracranial space by means of MR-morphometry. Vestnik Novgorodskogo Gosudarstvennogo Universiteta (Bulletin of Novgorod State University, Russian journal). 2015; 2 (85) (in Russ.).]

9. Лобзин В.Ю., Киселёв В.Н., Фокин В.А., Емелин А.Ю., Воробьёв С.В., Лупанов И.А. и др. Применение магнитно-резонансной морфометрии в диагностике болезни Альцгеймера и сосудистых когнитивных нарушений. Вестник Российской военно-медицинской академии. 2013; 3: 43. [Lobzin V.Yu., Kiselev V.N., Fokin V.A., Emelin A.Yu., Vorob’ev S.V., Lupanov I.A. et al. Application of magnetic resonance morphometry in the diagnosis of Alzheimer's disease and vascular cognitive disorders. Vestnik Rossiyskoy Voenno-Meditsinskoy Akademii (Bulletin of the Russian Military Medical Academy). 2013; 3: 43 (in Russ.).]

10. Одинак М.М., Воробьев С.В., Фокин В.А., Емелин А.Ю., Лобзин В.Ю., Соколов А.В. Магнитно-резонансная морфометрия в дифференциальной диагностике посттравматических когнитивных нарушений. Неврология, нейропсихиатрия, психосоматика. 2014; 2: 13–8. [Odinak M.M., Vorob’ev S.V., Fokin V.A., Emelin A.Yu., Lobzin V.Yu., Sokolov A.V. Magnetic resonance morphometry in the differential diagnosis of post-traumatic cognitive impairment. Nevrologiya, Neyropsikhiatriya, Psikhosomatika (Neurology, Neuropsychiatry, Psychosomatics, Russian journal). 2014; 2: 13–8 (in Russ.).]

11. Воронков Л.В., Труфанов А.Г., Фокин В.А., Литвиненко И.В., Одинак М.М., Ефимцев А.Ю. и др. Возможности воксель-базированной морфометрии в диагностике неопухолевых заболеваний головного мозга. Вестник Российской военно-медицинской академии. 2012; 1 (37). [Voronkov L.V., Trufanov A.G., Fokin V.A., Litvinenko I.V., Odinak M.M., Efimtsev A.Yu. et al. The possibilities of voxel-based morphometry in the diagnosis of neoplastic diseases of the brain. Vestnik Rossiyskoy Voenno-Meditsinskoy Akademii (Bulletin of the Russian Military Medical Academy). 2012; 1 (37) (in Russ.).]

12. Дамулина А.И., Коновалов Р.Н., Кадыков А.С. Значение воксельориентированной морфометрии в изучении умеренных когнитив- ных расстройств. Анналы клинической и экспериментальной неврологии. 2015; 9 (3): 42–8. [Damulina A.I., Konovalov R.N., Kadykov A.S. Value of voxel-oriented morphometry in the study of moderate cognitive disorders. Annaly Klinicheskoy i Eksperimental’noy Nevrologii (Annals of Clinical and Experimental Neurology, Russian journal). 2015; 9 (3): 42–8 (in Russ.).]

13. Alzheimer Disease Neuroimaging Initiative (ADNI). Available at: http://adni.loni.usc.edu

14. Weiner M.W., Veitch D.P., Aisen P.S., Beckett L.A., Cairns N.J., Greenj R.C. et al. The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimers Dement. 2012; 8 (10): S1–68. DOI: http://dx.doi.org/ 10.1016/j.jalz.2011.09.172

15. Desikan R.S., Cabral H.J., Settecase F., Hess Ch.P., Dillon W.P., Glastonbury Ch.M. et al. MRI measures predict progression to Alzheimer's disease. Neurobiol. Aging. 2010; 31 (8): 1364–74. DOI: 10.1016/j.neurobiolaging.2010.04.023

16. Cuingnet R., Gerardin E., Tessieras J., Auzias G., Lehéricy S., Habert M.-O. et al. and the Alzheimer's Disease Neuroimaging Initiative. Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 2010; 6: 4C.

17. Klöppel S., Stonnington C.M., Chu C., Draganski B., Scahill R.I., Rohrer J.D. et al. Automatic classification of MR scans in Alzheimer's disease. Brain. 2008; 131 (3): 681–9.

18. Vemuri P., Gunte J.L., Senjem M.L., Whitwell J.L., Kantarci K., Knopman D.S. et al. Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage. 2008; 39 (3): 1186–97.

19. Lao Z., Shen D., Xue Z., Karacali B., Resnick S.M., Davatzikos C. Morphological classification of brains via high-dimensional shape transformations and machine learning methods. Neuroimage. 2004; 21 (1): 46–57.

20. Fan Y., Shen D., Gur R.C., Davatzikosa C. COMPARE: classification of morphological patterns using adaptive regional elements. Med. Imag. 2007; 26 (1): 93–105.

21. Desikan R.S., Ségonne F., Fischl B., Quinn B.T., Dickerson B.C., Blacker D. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006; 31 (3): 968–80.

22. Desikan R.S., Cabral H.J., Hess C.P., Dillon W.P., Glastonbury C.M., Weiner M.W. et al., Alzheimer's Disease Neuroimaging Initiative. Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease. Brain. 2009; 132 (8): 2048–57.

23. Chupin M., Hammers A., Liu R.S., Colliot O., Burdett J., Bardinet E. et al., Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage. 2009; 46 (3): 749–61.

24. Chupin M., Gérardin E., Cuingnet R., Boutet C., Lemieux L., Lehéricy S. et al., Alzheimer's Disease Neuroimaging Initiative. Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI. Hippocampus. 2009; 19 (6): 579–87.

25. Gerardin E., Chételat G., Chupin M., Cuingnet R., Desgranges B., Kim H.-S. et al. Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging. Neuroimage. 2009; 47 (4): 1476–86.

26. Jie-Qiong Li, Lan Tan, Hui-Fu Wang, Meng-Shan Tan, Lin Tan, Wei Xu et al. Risk factors for predicting progression from mild cognitive impairment to Alzheimer’s disease: a systematic review and meta-analysis of cohort studies. Neurol. Neurosurg. Psychiatry. 2016; 87: 476–84.

27. Blanс F., Colloby S.J., Cretin B., Loureiro de Sousa P., Demuynck C., O’Brien J.T. et al. Grey matter atrophy in prodromal stage of dementia with Lewy bodies and Alzheimer’s disease. Alzheim. Res. Ther. 2016; 8: 31.

28. Дмитриев А.А., Кучерявский С.В. Применение методов многомерного анализа данных для диагностики ранних стадий болезни Альцгеймера по томограммам мозга. Управление, вычислительная техника и информатика. 2010; 1–2 (65). [Dmitriev A.A., Kucheryavskiy S.V. Application of multidimensional data analysis methods for diagnosis of early stages of Alzheimer's disease by brain tomograms. Upravlenie, Vychislitel’- naya Tekhnika i Informatika (Management, Computer Science and Informatics, Russian journal). 2010; 1–2 (65) (in Russ.).]

29. Leandrou S., Petroudi S., Kyriacou P.A., Reyes-Aldasorou C.C., Pattichis C.S. An overview of quantitative magnetic resonance imaging analysis studies in the assessment of Alzheimer's disease. Switzerland: Springer International Publishing; 2016.