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Компьютерный анализ данных магнитно-резонансной томографии головного мозга в верификации болезни мелких сосудов и умеренного когнитивного расстройства
Компьютерный анализ данных магнитно-резонансной томографии головного мозга в верификации болезни мелких сосудов и умеренного когнитивного расстройства
Крупенин П.М., Перепелов В.А., Перепелова Е.М., Бордовский С.П., Преображенская И.С., Соколова А.А., Напалков Д.А., Воскресенская О.Н. Компьютерный анализ данных магнитно-резонансной томографии головного мозга в верификации болезни мелких сосудов и умеренного когнитивного расстройства. Consilium Medicum. 2022;24(2):90–95. DOI: 10.26442/20751753.2022.2.201353
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Аннотация
Цель. Изучить возможности методов компьютерного анализа данных магнитно-резонансной томографии (МРТ) головного мозга у пациентов с болезнью мелких сосудов (БМС).
Материалы и методы. Обследован 31 пациент с БМС на фоне фибрилляции предсердий. Данные МРТ в стандартных режимах изучены с применением подходов сегментации очагов (Lesion Segmentation Tool) гиперинтенсивного белого вещества (ГИБВ) и расчета толщины коры больших полушарий в Computational Anatomy Toolbox для Statistical Parametric Mapping 12 (пакет программного обеспечения) в среде MATLAB. Оценку когнитивного статуса осуществляли при помощи Монреальской шкалы оценки когнитивных функций (Montreal Cognitive Assessment) и батареи тестов для объективизации гиппокампальных расстройств и нарушений управляющего домена когнитивных функций.
Результаты. У 16 (52%) пациентов диагностировано умеренное когнитивное расстройство сосудистого генеза. Медианное значение по визуальной шкале Фазекас составило 2 и 2 балла, медианная доля внутричерепного объема, занимаемая фракцией ГИБВ – 0,07%. Доля внутричерепного объема, занимаемая ГИБВ, коррелировала с производительностью в тестах управляющего домена. Толщина коры ряда кластеров префронтального комплекса и височной извилины коррелировала с результатами когнитивного тестирования. Среди расчетных МР-маркеров БМС наибольшей положительной предиктивной ценностью в отношении умеренного когнитивного расстройства обладала толщина коры затылочной доли с площадью под кривой, составившей 70%; среди когнитивных тестов – вспоминание с категориальной подсказкой, площадь под кривой 3,8%.
Заключение. Полученные данные могут служить валидным инструментом для оценки характеристик белого и серого вещества головного мозга и установления ряда закономерностей когнитивного функционирования у пациентов с БМС.
Ключевые слова: болезнь мелких церебральных сосудов, умеренное когнитивное расстройство, компьютерные методы анализа данных магнитно-резонансной томографии, толщина коры, гиперинтенсивность белого вещества
Materials and methods. Thirty-one patients underwent brain MRI in standard sequences. We used Lesion Segmentation Tool to assess white matter hyperintensities (WMH) volume and Computational Anatomy Toolbox to calculate cortical thickness. Both software plug-ins work within the Statistical Parametric Mapping 12 software for MATLAB. We also performed cognitive testing with the Montreal Cognitive Assessment test and tests to detect hippocampal and executive domain dysfunction.
Results. Sixteen patients had mild vascular cognitive impairment. The Median Fazekas scale score was 2 and 2 points. The median intracranial volume fraction occupied by the WMH was 0.07%. It correlated with the executive domain performance but not with cortical thickness. Cortical thickness within several clusters of the prefrontal complex and temporal lobe correlated with performance in cognitive tests. Among the computed MRI markers of the SVD, the occipital lobe cortical thickness had an area under the curve of 70%, and among the cognitive tests, the cued recall measure had an area under the curve of 73.8% to detect mild cognitive impairment.
Conclusion. The abovementioned metrics is a valuable tool to objectively estimate white and grey matter state in patients with small vessel disease. Performing those analyses helped to assess SVD properties in the sample further and register new correlations between MRI and cognitive markers.
Keywords: small vessel disease, mild cognitive impairment, computational magnetic resonance analysis, cortical thickness, white matter hyperintensities
Материалы и методы. Обследован 31 пациент с БМС на фоне фибрилляции предсердий. Данные МРТ в стандартных режимах изучены с применением подходов сегментации очагов (Lesion Segmentation Tool) гиперинтенсивного белого вещества (ГИБВ) и расчета толщины коры больших полушарий в Computational Anatomy Toolbox для Statistical Parametric Mapping 12 (пакет программного обеспечения) в среде MATLAB. Оценку когнитивного статуса осуществляли при помощи Монреальской шкалы оценки когнитивных функций (Montreal Cognitive Assessment) и батареи тестов для объективизации гиппокампальных расстройств и нарушений управляющего домена когнитивных функций.
Результаты. У 16 (52%) пациентов диагностировано умеренное когнитивное расстройство сосудистого генеза. Медианное значение по визуальной шкале Фазекас составило 2 и 2 балла, медианная доля внутричерепного объема, занимаемая фракцией ГИБВ – 0,07%. Доля внутричерепного объема, занимаемая ГИБВ, коррелировала с производительностью в тестах управляющего домена. Толщина коры ряда кластеров префронтального комплекса и височной извилины коррелировала с результатами когнитивного тестирования. Среди расчетных МР-маркеров БМС наибольшей положительной предиктивной ценностью в отношении умеренного когнитивного расстройства обладала толщина коры затылочной доли с площадью под кривой, составившей 70%; среди когнитивных тестов – вспоминание с категориальной подсказкой, площадь под кривой 3,8%.
Заключение. Полученные данные могут служить валидным инструментом для оценки характеристик белого и серого вещества головного мозга и установления ряда закономерностей когнитивного функционирования у пациентов с БМС.
Ключевые слова: болезнь мелких церебральных сосудов, умеренное когнитивное расстройство, компьютерные методы анализа данных магнитно-резонансной томографии, толщина коры, гиперинтенсивность белого вещества
________________________________________________
Materials and methods. Thirty-one patients underwent brain MRI in standard sequences. We used Lesion Segmentation Tool to assess white matter hyperintensities (WMH) volume and Computational Anatomy Toolbox to calculate cortical thickness. Both software plug-ins work within the Statistical Parametric Mapping 12 software for MATLAB. We also performed cognitive testing with the Montreal Cognitive Assessment test and tests to detect hippocampal and executive domain dysfunction.
Results. Sixteen patients had mild vascular cognitive impairment. The Median Fazekas scale score was 2 and 2 points. The median intracranial volume fraction occupied by the WMH was 0.07%. It correlated with the executive domain performance but not with cortical thickness. Cortical thickness within several clusters of the prefrontal complex and temporal lobe correlated with performance in cognitive tests. Among the computed MRI markers of the SVD, the occipital lobe cortical thickness had an area under the curve of 70%, and among the cognitive tests, the cued recall measure had an area under the curve of 73.8% to detect mild cognitive impairment.
Conclusion. The abovementioned metrics is a valuable tool to objectively estimate white and grey matter state in patients with small vessel disease. Performing those analyses helped to assess SVD properties in the sample further and register new correlations between MRI and cognitive markers.
Keywords: small vessel disease, mild cognitive impairment, computational magnetic resonance analysis, cortical thickness, white matter hyperintensities
Полный текст
Список литературы
1. Fazekas F, Chawluk JB, Alavi A, et al. MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol. 1987;149(2):351-6. DOI:10.2214/ajr.149.2.351
2. Wahlund LO, Barkhof F, Fazekas F, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke. 2001;32(6):1318-22. DOI:10.1161/01.STR.32.6.1318
3. Serag D, Ragab E. Bi-caudate ratio as a MRI marker of white matter atrophy in multiple sclerosis and ischemic leukocencephalopathy. Egypt J Radiol Nucl Med. 2019;50(1):99.
DOI:10.1186/s43055-019-0104-x
4. DeCarli C, Fletcher E, Ramey V, et al. Anatomical mapping of white matter hyperintensities (WMH): Exploring the relationships between periventricular WMH, deep WMH, and total WMH burden. Stroke. 2005;36(1):50-5. DOI:10.1161/01.STR.0000150668.58689.f2
5. Griffanti L, Jenkinson M, Suri S, et al. Classification and characterization of periventricular and deep white matter hyperintensities on MRI: A study in older adults. Neuroimage. 2018;170:174-81. DOI:10.1016/j.neuroimage.2017.03.024
6. Sachdev PS, Blacker D, Blazer DG, et al. Classifying neurocognitive disorders: The DSM-5 approach. Nat Rev Neurol. 2014;10(11):634-42. DOI:10.1038/nrneurol.2014.181
7. Nasreddine ZS, Phillips NA, Bedirian V, et al. The Montreal Cognitive Assessment, MoCA: A Brief Screening. J Am Geriatr Soc. 2005;53(4):695-9.
DOI:10.1111/j.1532-5415.2005.53221.x
8. Sarazin M, Berr C, De Rotrou J, et al. Amnestic syndrome of the medial temporal type identifies prodromal AD: A longitudinal study. Neurology. 2007;69(19):1859-67.
DOI:10.1212/01.wnl.0000279336.36610.f7
9. Reitan RM, Wolfson D. The Halstead-Reitan neuropsychological test battery: Theory and clinical interpretation. Tucson, AZ: Neuropsychology Press, 1985.
10. Smith A. Symbol Digit Modalities Test. Los Angeles, CA: Western Psychological Services, 1973.
11. Mohs RC, Knopman D, Petersen RC, et al. Development of cognitive instruments for use in clinical trials of antidementia drugs: additions to the Alzheimer’s Disease Assessment Scale that broaden its scope. The Alzheimer’s Disease Cooperative Study. Alzheimer Dis Assoc Disord. 1997;11 Suppl. 2:13-21.
12. Ferris SH. General measures of cognition. Int Psychogeriatr. 2003;15 Suppl. 1:215-7. DOI:10.1017/S1041610203009220
13. Starkstein SE, Mayberg HS, Preziosi TJ, et al. Reliability, validity, and clinical correlates of apathy in Parkinson’s disease. J Neuropsychiatry Clin Neurosci. 1992;4(2):134-9. DOI:10.1176/jnp.4.2.134
14. Yesavage JA, Brink TL, Rose TL, et al. Development and validation of a geriatric depression screening scale: A preliminary report. J Psychiatr Res. 1982-1983;17(1):37-49.
DOI:10.1016/0022-3956(82)90033-4
15. Schmidt P, Gaser C, Arsic M, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. Neuroimage. 2012;59(4):3774-83.
DOI:10.1016/j.neuroimage.2011.11.032
16. Gaser C, Dahnke R. CAT-A Computational Anatomy Toolbox for the Analysis of Structural MRI Data. 2016. Available at: http://www.neuro.uni-jena.de/hbm2016/GaserHBM2016.pdf. Accessed: 22.04.2022.
17. Dahnke R, Yotter RA, Gaser C. Cortical thickness and central surface estimation. Neuroimage. 2013;65:336-48. DOI:10.1016/j.neuroimage.2012.09.050
18. Yotter RA, Dahnke R, Thompson PM, Gaser C. Topological correction of brain surface meshes using spherical harmonics. Hum Brain Mapp. 2011;32(7):1109-24. DOI:10.1002/hbm.21095
19. Desikan RS, Segonne F, Fischl B, 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. DOI:10.1016/j.neuroimage.2006.01.021
20. Glasser MF, Coalson TS, Robinson EC, et al. A multi-modal parcellation of human cerebral cortex. Nature. 2016;536(7615):171-8. DOI:10.1038/nature18933
21. Llinas-Regla J, Vilalta-Franch J, Lopez-Pousa S, et al. The Trail Making Test: Association With Other Neuropsychological Measures and Normative Values for Adults Aged 55 Years and Older From a Spanish-Speaking Population-Based Sample. Assessment. 2017;24(2):183-96. DOI:10.1177/1073191115602552
22. Zhuang Y, Zeng X, Wang B, et al. Cortical surface thickness in the middle-aged brain with white matter hyperintense lesions. Front Aging Neurosci. 2017;9:225. DOI:10.3389/fnagi.2017.00225
23. Wang Y, Yang Y, Wang T, et al. Correlation between White Matter Hyperintensities Related Gray Matter Volume and Cognition in Cerebral Small Vessel Disease. J Stroke Cerebrovasc Dis. 2020;29(12):105275. DOI:10.1016/j.jstrokecerebrovasdis.2020.105275
24. Azarpazhooh MR, Hachinski V. Vascular cognitive impairment: A preventable component of dementia. Handb Clin Neurol. 2019;167:377-91.
DOI:10.1016/B978-0-12-804766-8.00020-0
25. Euston DR, Gruber AJ, McNaughton BL. The Role of Medial Prefrontal Cortex in Memory and Decision Making. Neuron. 2012;76(6):1057-70. DOI:10.1016/j.neuron.2012.12.002
26. Jagust W. Imaging the evolution and pathophysiology of Alzheimer disease. Nat Rev Neurosci. 2018;19(11):687-700. DOI:10.1038/s41583-018-0067-3
27. Roe JM, Vidal-Pineiro D, Sorensen O, et al. Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer’s disease. Nat Commun. 2021;12(1):721. DOI:10.1038/s41467-021-21057-y
28. Grambaite R, Selnes P, Reinvang I, et al. Executive Dysfunction in Mild Cognitive Impairment is Associated with Changes in Frontal and Cingulate White Matter Tracts. J Alzheimer’s Dis. 2011;27(2):453-62. DOI:10.3233/JAD-2011-110290
29. Tuladhar AM, van Norden AGW, de Laat KF, et al. White matter integrity in small vessel disease is related to cognition. NeuroImage Clin. 2015;7:518-24. DOI:10.1016/j.nicl.2015.02.003
2. Wahlund LO, Barkhof F, Fazekas F, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke. 2001;32(6):1318-22. DOI:10.1161/01.STR.32.6.1318
3. Serag D, Ragab E. Bi-caudate ratio as a MRI marker of white matter atrophy in multiple sclerosis and ischemic leukocencephalopathy. Egypt J Radiol Nucl Med. 2019;50(1):99.
DOI:10.1186/s43055-019-0104-x
4. DeCarli C, Fletcher E, Ramey V, et al. Anatomical mapping of white matter hyperintensities (WMH): Exploring the relationships between periventricular WMH, deep WMH, and total WMH burden. Stroke. 2005;36(1):50-5. DOI:10.1161/01.STR.0000150668.58689.f2
5. Griffanti L, Jenkinson M, Suri S, et al. Classification and characterization of periventricular and deep white matter hyperintensities on MRI: A study in older adults. Neuroimage. 2018;170:174-81. DOI:10.1016/j.neuroimage.2017.03.024
6. Sachdev PS, Blacker D, Blazer DG, et al. Classifying neurocognitive disorders: The DSM-5 approach. Nat Rev Neurol. 2014;10(11):634-42. DOI:10.1038/nrneurol.2014.181
7. Nasreddine ZS, Phillips NA, Bedirian V, et al. The Montreal Cognitive Assessment, MoCA: A Brief Screening. J Am Geriatr Soc. 2005;53(4):695-9.
DOI:10.1111/j.1532-5415.2005.53221.x
8. Sarazin M, Berr C, De Rotrou J, et al. Amnestic syndrome of the medial temporal type identifies prodromal AD: A longitudinal study. Neurology. 2007;69(19):1859-67.
DOI:10.1212/01.wnl.0000279336.36610.f7
9. Reitan RM, Wolfson D. The Halstead-Reitan neuropsychological test battery: Theory and clinical interpretation. Tucson, AZ: Neuropsychology Press, 1985.
10. Smith A. Symbol Digit Modalities Test. Los Angeles, CA: Western Psychological Services, 1973.
11. Mohs RC, Knopman D, Petersen RC, et al. Development of cognitive instruments for use in clinical trials of antidementia drugs: additions to the Alzheimer’s Disease Assessment Scale that broaden its scope. The Alzheimer’s Disease Cooperative Study. Alzheimer Dis Assoc Disord. 1997;11 Suppl. 2:13-21.
12. Ferris SH. General measures of cognition. Int Psychogeriatr. 2003;15 Suppl. 1:215-7. DOI:10.1017/S1041610203009220
13. Starkstein SE, Mayberg HS, Preziosi TJ, et al. Reliability, validity, and clinical correlates of apathy in Parkinson’s disease. J Neuropsychiatry Clin Neurosci. 1992;4(2):134-9. DOI:10.1176/jnp.4.2.134
14. Yesavage JA, Brink TL, Rose TL, et al. Development and validation of a geriatric depression screening scale: A preliminary report. J Psychiatr Res. 1982-1983;17(1):37-49.
DOI:10.1016/0022-3956(82)90033-4
15. Schmidt P, Gaser C, Arsic M, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. Neuroimage. 2012;59(4):3774-83.
DOI:10.1016/j.neuroimage.2011.11.032
16. Gaser C, Dahnke R. CAT-A Computational Anatomy Toolbox for the Analysis of Structural MRI Data. 2016. Available at: http://www.neuro.uni-jena.de/hbm2016/GaserHBM2016.pdf. Accessed: 22.04.2022.
17. Dahnke R, Yotter RA, Gaser C. Cortical thickness and central surface estimation. Neuroimage. 2013;65:336-48. DOI:10.1016/j.neuroimage.2012.09.050
18. Yotter RA, Dahnke R, Thompson PM, Gaser C. Topological correction of brain surface meshes using spherical harmonics. Hum Brain Mapp. 2011;32(7):1109-24. DOI:10.1002/hbm.21095
19. Desikan RS, Segonne F, Fischl B, 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. DOI:10.1016/j.neuroimage.2006.01.021
20. Glasser MF, Coalson TS, Robinson EC, et al. A multi-modal parcellation of human cerebral cortex. Nature. 2016;536(7615):171-8. DOI:10.1038/nature18933
21. Llinas-Regla J, Vilalta-Franch J, Lopez-Pousa S, et al. The Trail Making Test: Association With Other Neuropsychological Measures and Normative Values for Adults Aged 55 Years and Older From a Spanish-Speaking Population-Based Sample. Assessment. 2017;24(2):183-96. DOI:10.1177/1073191115602552
22. Zhuang Y, Zeng X, Wang B, et al. Cortical surface thickness in the middle-aged brain with white matter hyperintense lesions. Front Aging Neurosci. 2017;9:225. DOI:10.3389/fnagi.2017.00225
23. Wang Y, Yang Y, Wang T, et al. Correlation between White Matter Hyperintensities Related Gray Matter Volume and Cognition in Cerebral Small Vessel Disease. J Stroke Cerebrovasc Dis. 2020;29(12):105275. DOI:10.1016/j.jstrokecerebrovasdis.2020.105275
24. Azarpazhooh MR, Hachinski V. Vascular cognitive impairment: A preventable component of dementia. Handb Clin Neurol. 2019;167:377-91.
DOI:10.1016/B978-0-12-804766-8.00020-0
25. Euston DR, Gruber AJ, McNaughton BL. The Role of Medial Prefrontal Cortex in Memory and Decision Making. Neuron. 2012;76(6):1057-70. DOI:10.1016/j.neuron.2012.12.002
26. Jagust W. Imaging the evolution and pathophysiology of Alzheimer disease. Nat Rev Neurosci. 2018;19(11):687-700. DOI:10.1038/s41583-018-0067-3
27. Roe JM, Vidal-Pineiro D, Sorensen O, et al. Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer’s disease. Nat Commun. 2021;12(1):721. DOI:10.1038/s41467-021-21057-y
28. Grambaite R, Selnes P, Reinvang I, et al. Executive Dysfunction in Mild Cognitive Impairment is Associated with Changes in Frontal and Cingulate White Matter Tracts. J Alzheimer’s Dis. 2011;27(2):453-62. DOI:10.3233/JAD-2011-110290
29. Tuladhar AM, van Norden AGW, de Laat KF, et al. White matter integrity in small vessel disease is related to cognition. NeuroImage Clin. 2015;7:518-24. DOI:10.1016/j.nicl.2015.02.003
2. Wahlund LO, Barkhof F, Fazekas F, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke. 2001;32(6):1318-22. DOI:10.1161/01.STR.32.6.1318
3. Serag D, Ragab E. Bi-caudate ratio as a MRI marker of white matter atrophy in multiple sclerosis and ischemic leukocencephalopathy. Egypt J Radiol Nucl Med. 2019;50(1):99.
DOI:10.1186/s43055-019-0104-x
4. DeCarli C, Fletcher E, Ramey V, et al. Anatomical mapping of white matter hyperintensities (WMH): Exploring the relationships between periventricular WMH, deep WMH, and total WMH burden. Stroke. 2005;36(1):50-5. DOI:10.1161/01.STR.0000150668.58689.f2
5. Griffanti L, Jenkinson M, Suri S, et al. Classification and characterization of periventricular and deep white matter hyperintensities on MRI: A study in older adults. Neuroimage. 2018;170:174-81. DOI:10.1016/j.neuroimage.2017.03.024
6. Sachdev PS, Blacker D, Blazer DG, et al. Classifying neurocognitive disorders: The DSM-5 approach. Nat Rev Neurol. 2014;10(11):634-42. DOI:10.1038/nrneurol.2014.181
7. Nasreddine ZS, Phillips NA, Bedirian V, et al. The Montreal Cognitive Assessment, MoCA: A Brief Screening. J Am Geriatr Soc. 2005;53(4):695-9.
DOI:10.1111/j.1532-5415.2005.53221.x
8. Sarazin M, Berr C, De Rotrou J, et al. Amnestic syndrome of the medial temporal type identifies prodromal AD: A longitudinal study. Neurology. 2007;69(19):1859-67.
DOI:10.1212/01.wnl.0000279336.36610.f7
9. Reitan RM, Wolfson D. The Halstead-Reitan neuropsychological test battery: Theory and clinical interpretation. Tucson, AZ: Neuropsychology Press, 1985.
10. Smith A. Symbol Digit Modalities Test. Los Angeles, CA: Western Psychological Services, 1973.
11. Mohs RC, Knopman D, Petersen RC, et al. Development of cognitive instruments for use in clinical trials of antidementia drugs: additions to the Alzheimer’s Disease Assessment Scale that broaden its scope. The Alzheimer’s Disease Cooperative Study. Alzheimer Dis Assoc Disord. 1997;11 Suppl. 2:13-21.
12. Ferris SH. General measures of cognition. Int Psychogeriatr. 2003;15 Suppl. 1:215-7. DOI:10.1017/S1041610203009220
13. Starkstein SE, Mayberg HS, Preziosi TJ, et al. Reliability, validity, and clinical correlates of apathy in Parkinson’s disease. J Neuropsychiatry Clin Neurosci. 1992;4(2):134-9. DOI:10.1176/jnp.4.2.134
14. Yesavage JA, Brink TL, Rose TL, et al. Development and validation of a geriatric depression screening scale: A preliminary report. J Psychiatr Res. 1982-1983;17(1):37-49.
DOI:10.1016/0022-3956(82)90033-4
15. Schmidt P, Gaser C, Arsic M, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. Neuroimage. 2012;59(4):3774-83.
DOI:10.1016/j.neuroimage.2011.11.032
16. Gaser C, Dahnke R. CAT-A Computational Anatomy Toolbox for the Analysis of Structural MRI Data. 2016. Available at: http://www.neuro.uni-jena.de/hbm2016/GaserHBM2016.pdf. Accessed: 22.04.2022.
17. Dahnke R, Yotter RA, Gaser C. Cortical thickness and central surface estimation. Neuroimage. 2013;65:336-48. DOI:10.1016/j.neuroimage.2012.09.050
18. Yotter RA, Dahnke R, Thompson PM, Gaser C. Topological correction of brain surface meshes using spherical harmonics. Hum Brain Mapp. 2011;32(7):1109-24. DOI:10.1002/hbm.21095
19. Desikan RS, Segonne F, Fischl B, 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. DOI:10.1016/j.neuroimage.2006.01.021
20. Glasser MF, Coalson TS, Robinson EC, et al. A multi-modal parcellation of human cerebral cortex. Nature. 2016;536(7615):171-8. DOI:10.1038/nature18933
21. Llinas-Regla J, Vilalta-Franch J, Lopez-Pousa S, et al. The Trail Making Test: Association With Other Neuropsychological Measures and Normative Values for Adults Aged 55 Years and Older From a Spanish-Speaking Population-Based Sample. Assessment. 2017;24(2):183-96. DOI:10.1177/1073191115602552
22. Zhuang Y, Zeng X, Wang B, et al. Cortical surface thickness in the middle-aged brain with white matter hyperintense lesions. Front Aging Neurosci. 2017;9:225. DOI:10.3389/fnagi.2017.00225
23. Wang Y, Yang Y, Wang T, et al. Correlation between White Matter Hyperintensities Related Gray Matter Volume and Cognition in Cerebral Small Vessel Disease. J Stroke Cerebrovasc Dis. 2020;29(12):105275. DOI:10.1016/j.jstrokecerebrovasdis.2020.105275
24. Azarpazhooh MR, Hachinski V. Vascular cognitive impairment: A preventable component of dementia. Handb Clin Neurol. 2019;167:377-91.
DOI:10.1016/B978-0-12-804766-8.00020-0
25. Euston DR, Gruber AJ, McNaughton BL. The Role of Medial Prefrontal Cortex in Memory and Decision Making. Neuron. 2012;76(6):1057-70. DOI:10.1016/j.neuron.2012.12.002
26. Jagust W. Imaging the evolution and pathophysiology of Alzheimer disease. Nat Rev Neurosci. 2018;19(11):687-700. DOI:10.1038/s41583-018-0067-3
27. Roe JM, Vidal-Pineiro D, Sorensen O, et al. Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer’s disease. Nat Commun. 2021;12(1):721. DOI:10.1038/s41467-021-21057-y
28. Grambaite R, Selnes P, Reinvang I, et al. Executive Dysfunction in Mild Cognitive Impairment is Associated with Changes in Frontal and Cingulate White Matter Tracts. J Alzheimer’s Dis. 2011;27(2):453-62. DOI:10.3233/JAD-2011-110290
29. Tuladhar AM, van Norden AGW, de Laat KF, et al. White matter integrity in small vessel disease is related to cognition. NeuroImage Clin. 2015;7:518-24. DOI:10.1016/j.nicl.2015.02.003
________________________________________________
2. Wahlund LO, Barkhof F, Fazekas F, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke. 2001;32(6):1318-22. DOI:10.1161/01.STR.32.6.1318
3. Serag D, Ragab E. Bi-caudate ratio as a MRI marker of white matter atrophy in multiple sclerosis and ischemic leukocencephalopathy. Egypt J Radiol Nucl Med. 2019;50(1):99.
DOI:10.1186/s43055-019-0104-x
4. DeCarli C, Fletcher E, Ramey V, et al. Anatomical mapping of white matter hyperintensities (WMH): Exploring the relationships between periventricular WMH, deep WMH, and total WMH burden. Stroke. 2005;36(1):50-5. DOI:10.1161/01.STR.0000150668.58689.f2
5. Griffanti L, Jenkinson M, Suri S, et al. Classification and characterization of periventricular and deep white matter hyperintensities on MRI: A study in older adults. Neuroimage. 2018;170:174-81. DOI:10.1016/j.neuroimage.2017.03.024
6. Sachdev PS, Blacker D, Blazer DG, et al. Classifying neurocognitive disorders: The DSM-5 approach. Nat Rev Neurol. 2014;10(11):634-42. DOI:10.1038/nrneurol.2014.181
7. Nasreddine ZS, Phillips NA, Bedirian V, et al. The Montreal Cognitive Assessment, MoCA: A Brief Screening. J Am Geriatr Soc. 2005;53(4):695-9.
DOI:10.1111/j.1532-5415.2005.53221.x
8. Sarazin M, Berr C, De Rotrou J, et al. Amnestic syndrome of the medial temporal type identifies prodromal AD: A longitudinal study. Neurology. 2007;69(19):1859-67.
DOI:10.1212/01.wnl.0000279336.36610.f7
9. Reitan RM, Wolfson D. The Halstead-Reitan neuropsychological test battery: Theory and clinical interpretation. Tucson, AZ: Neuropsychology Press, 1985.
10. Smith A. Symbol Digit Modalities Test. Los Angeles, CA: Western Psychological Services, 1973.
11. Mohs RC, Knopman D, Petersen RC, et al. Development of cognitive instruments for use in clinical trials of antidementia drugs: additions to the Alzheimer’s Disease Assessment Scale that broaden its scope. The Alzheimer’s Disease Cooperative Study. Alzheimer Dis Assoc Disord. 1997;11 Suppl. 2:13-21.
12. Ferris SH. General measures of cognition. Int Psychogeriatr. 2003;15 Suppl. 1:215-7. DOI:10.1017/S1041610203009220
13. Starkstein SE, Mayberg HS, Preziosi TJ, et al. Reliability, validity, and clinical correlates of apathy in Parkinson’s disease. J Neuropsychiatry Clin Neurosci. 1992;4(2):134-9. DOI:10.1176/jnp.4.2.134
14. Yesavage JA, Brink TL, Rose TL, et al. Development and validation of a geriatric depression screening scale: A preliminary report. J Psychiatr Res. 1982-1983;17(1):37-49.
DOI:10.1016/0022-3956(82)90033-4
15. Schmidt P, Gaser C, Arsic M, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. Neuroimage. 2012;59(4):3774-83.
DOI:10.1016/j.neuroimage.2011.11.032
16. Gaser C, Dahnke R. CAT-A Computational Anatomy Toolbox for the Analysis of Structural MRI Data. 2016. Available at: http://www.neuro.uni-jena.de/hbm2016/GaserHBM2016.pdf. Accessed: 22.04.2022.
17. Dahnke R, Yotter RA, Gaser C. Cortical thickness and central surface estimation. Neuroimage. 2013;65:336-48. DOI:10.1016/j.neuroimage.2012.09.050
18. Yotter RA, Dahnke R, Thompson PM, Gaser C. Topological correction of brain surface meshes using spherical harmonics. Hum Brain Mapp. 2011;32(7):1109-24. DOI:10.1002/hbm.21095
19. Desikan RS, Segonne F, Fischl B, 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. DOI:10.1016/j.neuroimage.2006.01.021
20. Glasser MF, Coalson TS, Robinson EC, et al. A multi-modal parcellation of human cerebral cortex. Nature. 2016;536(7615):171-8. DOI:10.1038/nature18933
21. Llinas-Regla J, Vilalta-Franch J, Lopez-Pousa S, et al. The Trail Making Test: Association With Other Neuropsychological Measures and Normative Values for Adults Aged 55 Years and Older From a Spanish-Speaking Population-Based Sample. Assessment. 2017;24(2):183-96. DOI:10.1177/1073191115602552
22. Zhuang Y, Zeng X, Wang B, et al. Cortical surface thickness in the middle-aged brain with white matter hyperintense lesions. Front Aging Neurosci. 2017;9:225. DOI:10.3389/fnagi.2017.00225
23. Wang Y, Yang Y, Wang T, et al. Correlation between White Matter Hyperintensities Related Gray Matter Volume and Cognition in Cerebral Small Vessel Disease. J Stroke Cerebrovasc Dis. 2020;29(12):105275. DOI:10.1016/j.jstrokecerebrovasdis.2020.105275
24. Azarpazhooh MR, Hachinski V. Vascular cognitive impairment: A preventable component of dementia. Handb Clin Neurol. 2019;167:377-91.
DOI:10.1016/B978-0-12-804766-8.00020-0
25. Euston DR, Gruber AJ, McNaughton BL. The Role of Medial Prefrontal Cortex in Memory and Decision Making. Neuron. 2012;76(6):1057-70. DOI:10.1016/j.neuron.2012.12.002
26. Jagust W. Imaging the evolution and pathophysiology of Alzheimer disease. Nat Rev Neurosci. 2018;19(11):687-700. DOI:10.1038/s41583-018-0067-3
27. Roe JM, Vidal-Pineiro D, Sorensen O, et al. Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer’s disease. Nat Commun. 2021;12(1):721. DOI:10.1038/s41467-021-21057-y
28. Grambaite R, Selnes P, Reinvang I, et al. Executive Dysfunction in Mild Cognitive Impairment is Associated with Changes in Frontal and Cingulate White Matter Tracts. J Alzheimer’s Dis. 2011;27(2):453-62. DOI:10.3233/JAD-2011-110290
29. Tuladhar AM, van Norden AGW, de Laat KF, et al. White matter integrity in small vessel disease is related to cognition. NeuroImage Clin. 2015;7:518-24. DOI:10.1016/j.nicl.2015.02.003
Авторы
П.М. Крупенин*, В.А. Перепелов, Е.М. Перепелова, С.П. Бордовский, И.С. Преображенская, А.А. Соколова, Д.А. Напалков, О.Н. Воскресенская
ФГАОУ ВО «Первый Московский государственный медицинский университет им. И.М. Сеченова» Минздрава России (Сеченовский Университет), Москва, Россия
*vsevolod.perepelov@gmail.com
Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
*vsevolod.perepelov@gmail.com
ФГАОУ ВО «Первый Московский государственный медицинский университет им. И.М. Сеченова» Минздрава России (Сеченовский Университет), Москва, Россия
*vsevolod.perepelov@gmail.com
________________________________________________
Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
*vsevolod.perepelov@gmail.com
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