<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "JATS-journalpublishing1-3.dtd">
<article article-type="review-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">medsovet</journal-id><journal-title-group><journal-title xml:lang="ru">Медицинский Совет</journal-title><trans-title-group xml:lang="en"><trans-title>Meditsinskiy sovet = Medical Council</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2079-701X</issn><issn pub-type="epub">2658-5790</issn><publisher><publisher-name>REMEDIUM GROUP Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.21518/ms2024-289</article-id><article-id custom-type="elpub" pub-id-type="custom">medsovet-8465</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>КОГНИТИВНЫЕ РАССТРОЙСТВА</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>COGNITIVE DISRODERS</subject></subj-group></article-categories><title-group><article-title>Возможности и перспективы метода магнитно-резонансной морфометрии в диагностике деменций</article-title><trans-title-group xml:lang="en"><trans-title>Possibilities and prospects of the MR morphometry method in the diagnosis of dementia</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-0074-4021</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Терновых</surname><given-names>И. К.</given-names></name><name name-style="western" xml:lang="en"><surname>Ternovykh</surname><given-names>I. К.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Терновых Иван Константинович, аспирант кафедры неврологии с клиникой, младший научный сотрудник научно-исследовательской лаборатории неврологии и нейрореабилитации</p><p>197341, Санкт-Петербург, ул. Аккуратова, д. 2</p></bio><bio xml:lang="en"><p>Ivan К. Ternovykh, Postgraduate Student of the Department of Neurology with a clinic, Junior Researcher at the Research Laboratory of Neurology and Neurorehabilitation</p><p>2, Akkuratov St., St Petersburg, 197341</p></bio><email xlink:type="simple">rik.2006@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-4830-907X</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Воробьев</surname><given-names>С. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Vorobyev</surname><given-names>S. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p> </p><p>Воробьев Сергей Владимирович, д.м.н., главный научный сотрудник научно-исследовательской лаборатории неврологии и нейрореабилитации, профессор кафедры неврологии с клиникой; профессор кафедры клинической лабораторной диагностики</p><p>197341, Санкт-Петербург, ул. Аккуратова, д. 2</p><p>194100, Санкт-Петербург, ул. Литовская, д. 2</p></bio><bio xml:lang="en"><p>Sergey V. Vorobyev, Dr. Sci. (Med.), Chief Researcher of the Research Laboratory of Neurology and Neurorehabilitation, Professor of the Department of Neurology with clinic; Professor of the Department of Clinical Laboratory Diagnostics</p><p>2, Akkuratov St., St Petersburg, 197341</p><p>2, Litovskaya St., St Petersburg, 194100</p></bio><email xlink:type="simple">sergiognezdo@yandex.ru</email><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-6484-286X</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Янишевский</surname><given-names>С. Н.</given-names></name><name name-style="western" xml:lang="en"><surname>Yanishevskiy</surname><given-names>S. N.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Янишевский Станислав Николаевич, д.м.н., заведующий научно-исследовательской лаборатории неврологии и нейрореабилитации, профессор кафедры неврологии с клиникой</p><p>197341, Санкт-Петербург, ул. Аккуратова, д. 2</p></bio><bio xml:lang="en"><p>Stanislav N. Yanishevskiy, Dr. Sci. (Med.), Head of the Research Laboratory of Neurology and Neurorehabilitation, Professor of the Department of Neurology with сlinic</p><p>2, Akkuratov St., St Petersburg, 197341</p></bio><email xlink:type="simple">stasya71@yandex.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-4149-0029</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Танташева</surname><given-names>А. М.</given-names></name><name name-style="western" xml:lang="en"><surname>Tantasheva</surname><given-names>A. M.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Танташева Анна Мухамеджановна, врач-невролог консультативно-диагностического центра Университетской клиники</p><p>197341, Санкт-Петербург, ул. Аккуратова, д. 2</p></bio><bio xml:lang="en"><p>Anna М. Tantasheva, Neurologist of the Consultative and Diagnostic Center of the University Clinic</p><p>2, Akkuratov St., St Petersburg, 197341</p></bio><email xlink:type="simple">tantasheva_a@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-7336-3860</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Шубина</surname><given-names>К. М.</given-names></name><name name-style="western" xml:lang="en"><surname>Shubina</surname><given-names>К. M.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Шубина Кристина Максимовна, аспирант кафедры неврологии с клиникой</p><p>197341, Санкт-Петербург, ул. Аккуратова, д. 2</p></bio><bio xml:lang="en"><p>Kristina M. Shubina, Postgraduate Student of the Department of Neurology with clinic</p><p>2, Akkuratov St., St Petersburg, 197341</p></bio><email xlink:type="simple">krisschubina@yandex.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-4456-0398</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Антушева</surname><given-names>М. С.</given-names></name><name name-style="western" xml:lang="en"><surname>Antusheva</surname><given-names>M. S.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Антушева Мария Сергеевна, студент Института медицинского образования</p><p>197341, Санкт-Петербург, ул. Аккуратова, д. 2</p></bio><bio xml:lang="en"><p>Maria S. Antusheva, Student of the Institute of Medical Education</p><p>2, Akkuratov St., St Petersburg, 197341</p></bio><email xlink:type="simple">mariantusheva@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0004-1702-7397</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Сатиева</surname><given-names>М. Г.</given-names></name><name name-style="western" xml:lang="en"><surname>Satieva</surname><given-names>M. G.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Сатиева Марина Гаруновна, врач-невролог</p><p>197341, Санкт-Петербург, ул. Аккуратова, д. 2</p></bio><bio xml:lang="en"><p>Marina G. Satieva, Neurologist</p><p>2, Akkuratov St., St Petersburg, 197341</p></bio><email xlink:type="simple">dr.satieva_m@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Национальный медицинский исследовательский центр имени В.А. Алмазова</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Almazov National Medical Research Centre</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Национальный медицинский исследовательский центр имени В.А. Алмазова; Санкт-Петербургский государственный педиатрический медицинский университет</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Almazov National Medical Research Centre; St Petersburg State Pediatric Medical University</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2024</year></pub-date><pub-date pub-type="epub"><day>14</day><month>08</month><year>2024</year></pub-date><volume>0</volume><issue>12</issue><elocation-id>22–30</elocation-id><permissions><copyright-statement>Copyright &amp;#x00A9; Терновых И.К., Воробьев С.В., Янишевский С.Н., Танташева А.М., Шубина К.М., Антушева М.С., Сатиева М.Г., 2024</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="ru">Терновых И.К., Воробьев С.В., Янишевский С.Н., Танташева А.М., Шубина К.М., Антушева М.С., Сатиева М.Г.</copyright-holder><copyright-holder xml:lang="en">Ternovykh I.К., Vorobyev S.V., Yanishevskiy S.N., Tantasheva A.M., Shubina К.M., Antusheva M.S., Satieva M.G.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://www.med-sovet.pro/jour/article/view/8465">https://www.med-sovet.pro/jour/article/view/8465</self-uri><abstract><p>Деменция в настоящее время рассматривается в качестве одной из важнейших причин снижения качества бытовой, социальной адаптации пациентов, нарушения их возможностей функционирования в окружающем мире. Она может формироваться при целом спектре заболеваний, основными из которых являются нейродегенеративные процессы, в частности болезнь Альцгеймера. В диагностике когнитивных расстройств, кроме клинического и нейропсихологического, а также лабораторного обследования, существенную роль играют современные методы компьютерной нейровизуализации. Существенное значение при этом придается исследованиям на основе магнитного резонанса, достигшим большого прогресса за последнее время. Изменения, регистрируемые с помощью структурной магнитно-резонансной томографии (МРТ), зачастую «отстают» от регистрируемой клинической симптоматики и определяются на развернутых этапах болезни. Для верификации ранних проявлений нейродегенеративных изменений, наблюдающихся в головном мозге, предложены новые методы МРТ. К ним, в частности, относится магнитно-резонансная (МР)морфометрия. Она позволяет определить абсолютные значения выраженности атрофических изменений, установить их точную топическую локализацию. Ее применение не только улучшает возможности постановки диагноза на додементных стадиях, но также повышает качество дифференциальной диагностики различных патогенетических вариантов наблюдающихся когнитивных нарушений. Кроме того, применение специальных программ постпроцессинговой обработки данных уменьшает вероятность ошибки при интерпретации полученных данных. В настоящее время предложены три варианта оценки результатов МР-морфометрии, основанных на анализе объема отдельных структур головного мозга, толщины серого вещества, а также пространственной формы тех или иных формирований. Комплексное использование этих вариантов способствует наиболее качественному МР-морфометрическому обследованию головного мозга при формировании нейродегенеративного процесса.</p></abstract><trans-abstract xml:lang="en"><p>Dementia is one variant severe disorder’s higher cortical function and is currently considered as one most important cause a decrease in the quality household, social adaptation patients, and a violation their ability function in the world around them. It can be formed in a wide range disease, main which are neurodegenerative processes, in particular Alzheimer’s disease. In the diagnosis cognitive disorders, in addition to clinical and neuropsychological, as well as laboratory examination, modern methods computer neuroimaging plays an essential role. Same time, significant importance is attached research based on magnetic resonance, which has achieved great progress in recent years. Changes recorded by structural magnetic resonance imaging (MR tomography) often “lag behind” recorded clinical symptoms and are determined advanced disease. New MR methods have been proposed to verify early manifestations of neurodegenerative changes observed in the brain. These include, in particular, MR morphometry. It allows you to determine absolute values severity of atrophic changes, to establish their exact topical localization. Its use not only improves the possibilities of diagnosis pre-stage stages, but also improves quality of differential diagnosis various pathogenetic variants observed cognitive disorders. In addition, use special postprocessing data processing programs reduce the likelihood errors in interpretation received data. Currently, three options have been proposed for evaluating the results of MR morphometry, based on the analysis volume individual brain structures, thickness gray matter, as well as the spatial shape certain formations. Integrated use these options contributes highest quality MR morphometric examination of the brain during formation neurodegenerative process.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>нейродегенерация</kwd><kwd>деменция</kwd><kwd>когнитивные нарушения</kwd><kwd>магнитно-резонансная томография</kwd><kwd>МР-морфометрия</kwd><kwd>болезнь Альцгеймера</kwd></kwd-group><kwd-group xml:lang="en"><kwd>neurodegeneration</kwd><kwd>dementia</kwd><kwd>cognitive impairment</kwd><kwd>magnetic resonance imaging</kwd><kwd>MR morphometry</kwd><kwd>Alzheimer’s disease</kwd></kwd-group><funding-group><funding-statement xml:lang="ru">Работа выполнена в рамках Государственного задания 124021600050-1.</funding-statement><funding-statement xml:lang="en">This work was performed as part of State Assignment 124021600050-1.</funding-statement></funding-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Martin-Macintosh EL, Broski SM, Johnson GB, Hunt CH, Cullen EL, Peller PJ. Multimodality Imaging of Neurodegenerative Processes: Part 1, The Basics and Common Dementias. AJR Am J Roentgenol. 2016;207(4):871–882. https://doi.org/10.2214/AJR.14.12842.</mixed-citation><mixed-citation xml:lang="en">Martin-Macintosh EL, Broski SM, Johnson GB, Hunt CH, Cullen EL, Peller PJ. Multimodality Imaging of Neurodegenerative Processes: Part 1, The Basics and Common Dementias. AJR Am J Roentgenol. 2016;207(4):871–882. https://doi.org/10.2214/AJR.14.12842.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Ranganathan LN, Guhan R, Arun Shivaraman MM, Sankar PL, Srinivasan AV, Suriyakumar G, Periakaruppan AL. Changing Landscapes in the Neuroimaging of Dementia. Ann Indian Acad Neurol. 2018;21(2):98–106. https://doi.org/10.4103/aian.AIAN_48_18.</mixed-citation><mixed-citation xml:lang="en">Ranganathan LN, Guhan R, Arun Shivaraman MM, Sankar PL, Srinivasan AV, Suriyakumar G, Periakaruppan AL. Changing Landscapes in the Neuroimaging of Dementia. Ann Indian Acad Neurol. 2018;21(2):98–106. https://doi.org/10.4103/aian.AIAN_48_18.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Прокопенко СВ, Баранкин БВ, Марьина НМ, Можейко ЕЮ, Зубрицкая ЕМ, Чанчикова НГ и др. Диагностика болезни Альцгеймера с использованием ПЭТ/КТ: клиническое наблюдение. Сибирское медицинское обозрение. 2018;(6):67–73. https://doi.org/10.20333/2500136-2018-6-67-73.</mixed-citation><mixed-citation xml:lang="en">Prokopenko SV, Barankin BV, Mar’ina NM, Mozheyko EYu, Zubritskaya EM, Chanchikova NG et al. Diagnostics of Alzheimer’s disease using PET/CT: clinical survey. Siberian Medical Review. 2018;(6):67–73. (In Russ.) https://doi.org/10.20333/2500136-2018-6-67-73.</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Bao W, Xie F, Zuo C, Guan Y, Huang YH. PET Neuroimaging of Alzheimer’s Disease: Radiotracers and Their Utility in Clinical Research. Front Aging Neurosci. 2021;13:624330. https://doi.org/10.3389/fnagi.2021.624330.</mixed-citation><mixed-citation xml:lang="en">Bao W, Xie F, Zuo C, Guan Y, Huang YH. PET Neuroimaging of Alzheimer’s Disease: Radiotracers and Their Utility in Clinical Research. Front Aging Neurosci. 2021;13:624330. https://doi.org/10.3389/fnagi.2021.624330.</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Rayment D, Biju M, Zheng R, Kuruvilla T. Neuroimaging in dementia: An update for the general clinician. Prog Neurol Psychiatry. 2016;20:16–20. https://doi.org/10.1002/pnp.420.</mixed-citation><mixed-citation xml:lang="en">Rayment D, Biju M, Zheng R, Kuruvilla T. Neuroimaging in dementia: An update for the general clinician. Prog Neurol Psychiatry. 2016;20:16–20. https://doi.org/10.1002/pnp.420.</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Jack CR, Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB et al. A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016;87(5):539–547. https://doi.org/10.1212/WNL.0000000000002923.</mixed-citation><mixed-citation xml:lang="en">Jack CR, Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB et al. A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016;87(5):539–547. https://doi.org/10.1212/WNL.0000000000002923.</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Harper L, Barkhof F, Scheltens P, Schott JM, Fox NC. An algorithmic approach to structural imaging in dementia. J Neurol Neurosurg Psychiatry. 2014;85(6):692–698. https://doi.org/10.1136/jnnp-2013-306285.</mixed-citation><mixed-citation xml:lang="en">Harper L, Barkhof F, Scheltens P, Schott JM, Fox NC. An algorithmic approach to structural imaging in dementia. J Neurol Neurosurg Psychiatry. 2014;85(6):692–698. https://doi.org/10.1136/jnnp-2013-306285.</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Harper L, Barkhof F, Fox NC, Schott JM. Using visual rating to diagnose dementia: a critical evaluation of MRI atrophy scales. J Neurol Neurosurg Psychiatry. 2015;86(11):1225–1233. https://doi.org/10.1136/jnnp-2014-310090.</mixed-citation><mixed-citation xml:lang="en">Harper L, Barkhof F, Fox NC, Schott JM. Using visual rating to diagnose dementia: a critical evaluation of MRI atrophy scales. J Neurol Neurosurg Psychiatry. 2015;86(11):1225–1233. https://doi.org/10.1136/jnnp-2014-310090.</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013;12(8):822–838. https://doi.org/10.1016/S1474-4422(13)70124-8.</mixed-citation><mixed-citation xml:lang="en">Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013;12(8):822–838. https://doi.org/10.1016/S1474-4422(13)70124-8.</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Sachdev PS, Zhuang L, Braidy N, Wen W. Is Alzheimer’s a disease of the white matter? Curr Opin Psychiatry. 2013;26(3):244–251. https://doi.org/10.1097/YCO.0b013e32835ed6e8.</mixed-citation><mixed-citation xml:lang="en">Sachdev PS, Zhuang L, Braidy N, Wen W. Is Alzheimer’s a disease of the white matter? Curr Opin Psychiatry. 2013;26(3):244–251. https://doi.org/10.1097/YCO.0b013e32835ed6e8.</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Madden DJ, Bennett IJ, Burzynska A, Potter GG, Chen NK, Song AW. Diffusion tensor imaging of cerebral white matter integrity in cognitive aging. Biochim Biophys Acta. 2012;1822(3):386–400. https://doi.org/10.1016/j.bbadis.2011.08.003.</mixed-citation><mixed-citation xml:lang="en">Madden DJ, Bennett IJ, Burzynska A, Potter GG, Chen NK, Song AW. Diffusion tensor imaging of cerebral white matter integrity in cognitive aging. Biochim Biophys Acta. 2012;1822(3):386–400. https://doi.org/10.1016/j.bbadis.2011.08.003.</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Medina DA, Gaviria M. Diffusion tensor imaging investigations in Alzheimer’s disease: the resurgence of white matter compromise in the cortical dysfunction of the aging brain. Neuropsychiatr Dis Treat. 2008;4(4):737–742. https://doi.org/10.2147/ndt.s3381.</mixed-citation><mixed-citation xml:lang="en">Medina DA, Gaviria M. Diffusion tensor imaging investigations in Alzheimer’s disease: the resurgence of white matter compromise in the cortical dysfunction of the aging brain. Neuropsychiatr Dis Treat. 2008;4(4):737–742. https://doi.org/10.2147/ndt.s3381.</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Evans MC, Barnes J, Nielsen C, Kim LG, Clegg SL, Blair M et al. Volume changes in Alzheimer’s disease and mild cognitive impairment: cognitive associations. Eur Radiol. 2010;20(3):674–682. https://doi.org/10.1007/s00330-009-1581-5.</mixed-citation><mixed-citation xml:lang="en">Evans MC, Barnes J, Nielsen C, Kim LG, Clegg SL, Blair M et al. Volume changes in Alzheimer’s disease and mild cognitive impairment: cognitive associations. Eur Radiol. 2010;20(3):674–682. https://doi.org/10.1007/s00330-009-1581-5.</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Whitwell JL, Shiung MM, Przybelski SA, Weigand SD, Knopman DS, Boeve BF et al. MRI patterns of atrophy associated with progression to AD in amnestic mild cognitive impairment. Neurology. 2008;70(7):512–520. https://doi.org/10.1212/01.wnl.0000280575.77437.a2.</mixed-citation><mixed-citation xml:lang="en">Whitwell JL, Shiung MM, Przybelski SA, Weigand SD, Knopman DS, Boeve BF et al. MRI patterns of atrophy associated with progression to AD in amnestic mild cognitive impairment. Neurology. 2008;70(7):512–520. https://doi.org/10.1212/01.wnl.0000280575.77437.a2.</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Busatto GF, Garrido GE, Almeida OP, Castro CC, Camargo CH, Cid CG et al. A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer’s disease. Neurobiol Aging. 2003;24(2):221–231. https://doi.org/10.1016/s0197-4580(02)00084-2.</mixed-citation><mixed-citation xml:lang="en">Busatto GF, Garrido GE, Almeida OP, Castro CC, Camargo CH, Cid CG et al. A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer’s disease. Neurobiol Aging. 2003;24(2):221–231. https://doi.org/10.1016/s0197-4580(02)00084-2.</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Dickerson BC, Goncharova I, Sullivan MP, Forchetti C, Wilson RS, Bennett DA et al. MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer’s disease. Neurobiol Aging. 2001;22(5):747–754. https://doi.org/10.1016/s0197-4580(01)00271-8.</mixed-citation><mixed-citation xml:lang="en">Dickerson BC, Goncharova I, Sullivan MP, Forchetti C, Wilson RS, Bennett DA et al. MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer’s disease. Neurobiol Aging. 2001;22(5):747–754. https://doi.org/10.1016/s0197-4580(01)00271-8.</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Juottonen K, Laakso MP, Insausti R, Lehtovirta M, Pitkänen A, Partanen K, Soininen H. Volumes of the entorhinal and perirhinal cortices in Alzheimer’s disease. Neurobiol Aging. 1998;19(1):15–22. https://doi.org/10.1016/s0197-4580(98)00007-4.</mixed-citation><mixed-citation xml:lang="en">Juottonen K, Laakso MP, Insausti R, Lehtovirta M, Pitkänen A, Partanen K, Soininen H. Volumes of the entorhinal and perirhinal cortices in Alzheimer’s disease. Neurobiol Aging. 1998;19(1):15–22. https://doi.org/10.1016/s0197-4580(98)00007-4.</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Killiany RJ, Moss MB, Albert MS, Sandor T, Tieman J, Jolesz F. Temporal lobe regions on magnetic resonance imaging identify patients with early Alzheimer’s disease. Arch Neurol. 1993;50(9):949–954. https://doi.org/10.1001/archneur.1993.00540090052010.</mixed-citation><mixed-citation xml:lang="en">Killiany RJ, Moss MB, Albert MS, Sandor T, Tieman J, Jolesz F. Temporal lobe regions on magnetic resonance imaging identify patients with early Alzheimer’s disease. Arch Neurol. 1993;50(9):949–954. https://doi.org/10.1001/archneur.1993.00540090052010.</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Karas GB, Burton EJ, Rombouts SA, van Schijndel RA, O’Brien JT, Scheltens Ph et al. A comprehensive study of gray matter loss in patients with Alzheimer’s disease using optimized voxel-based morphometry. Neuroimage. 2003;18(4):895–907. https://doi.org/10.1016/s1053-8119(03)00041-7.</mixed-citation><mixed-citation xml:lang="en">Karas GB, Burton EJ, Rombouts SA, van Schijndel RA, O’Brien JT, Scheltens Ph et al. A comprehensive study of gray matter loss in patients with Alzheimer’s disease using optimized voxel-based morphometry. Neuroimage. 2003;18(4):895–907. https://doi.org/10.1016/s1053-8119(03)00041-7.</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Braak H, Braak E.Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82(4):239–259. https://doi.org/10.1007/BF00308809.</mixed-citation><mixed-citation xml:lang="en">Braak H, Braak E.Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82(4):239–259. https://doi.org/10.1007/BF00308809.</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Braak E, Griffing K, Arai K, Bohl J, Bratzke H, Braak H. Neuropathology of Alzheimer’s disease: what is new since A. Alzheimer? Eur Arch Psychiatry Clin Neurosci. 1999;249(3):14–22. https://doi.org/10.1007/pl00014168.</mixed-citation><mixed-citation xml:lang="en">Braak E, Griffing K, Arai K, Bohl J, Bratzke H, Braak H. Neuropathology of Alzheimer’s disease: what is new since A. Alzheimer? Eur Arch Psychiatry Clin Neurosci. 1999;249(3):14–22. https://doi.org/10.1007/pl00014168.</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">Karas GB, Scheltens P, Rombouts SA, Visser PJ, van Schijndel RA, Fox NC, Barkhof F. Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. Neuroimage. 2004;23(2):708–716. https://doi.org/10.1016/j.neuroimage.2004.07.006.</mixed-citation><mixed-citation xml:lang="en">Karas GB, Scheltens P, Rombouts SA, Visser PJ, van Schijndel RA, Fox NC, Barkhof F. Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. Neuroimage. 2004;23(2):708–716. https://doi.org/10.1016/j.neuroimage.2004.07.006.</mixed-citation></citation-alternatives></ref><ref id="cit23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">Bernardes da Silva FRS, Barbosa OJH, Rondinoni C, Dos Santos AC, Garrido Salmon CE, da Costa Lima NK et al. Neuro-degeneration profile of Alzheimer’s patients: A brain morphometry study. Neuroimage Clin. 2017;15:15–24. https://doi.org/10.1016/j.nicl.2017.04.001.</mixed-citation><mixed-citation xml:lang="en">Bernardes da Silva FRS, Barbosa OJH, Rondinoni C, Dos Santos AC, Garrido Salmon CE, da Costa Lima NK et al. Neuro-degeneration profile of Alzheimer’s patients: A brain morphometry study. Neuroimage Clin. 2017;15:15–24. https://doi.org/10.1016/j.nicl.2017.04.001.</mixed-citation></citation-alternatives></ref><ref id="cit24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">Blair JC, Lasiecka ZM, Patrie J, Barrett MJ, Druzgal TJ. Cytoarchitectonic Mapping of MRI Detects Rapid Changes in Alzheimer’s Disease. Front Neurol. 2020;11:241. https://doi.org/10.3389/fneur.2020.00241.</mixed-citation><mixed-citation xml:lang="en">Blair JC, Lasiecka ZM, Patrie J, Barrett MJ, Druzgal TJ. Cytoarchitectonic Mapping of MRI Detects Rapid Changes in Alzheimer’s Disease. Front Neurol. 2020;11:241. https://doi.org/10.3389/fneur.2020.00241.</mixed-citation></citation-alternatives></ref><ref id="cit25"><label>25</label><citation-alternatives><mixed-citation xml:lang="ru">Robert V, Cassim S, Chevaleyre V, Piskorowski RA. Hippocampal area CA2: properties and contribution to hippocampal function. Cell Tissue Res. 2018;373(3):525–540. https://doi.org/10.1007/s00441-017-2769-7.</mixed-citation><mixed-citation xml:lang="en">Robert V, Cassim S, Chevaleyre V, Piskorowski RA. Hippocampal area CA2: properties and contribution to hippocampal function. Cell Tissue Res. 2018;373(3):525–540. https://doi.org/10.1007/s00441-017-2769-7.</mixed-citation></citation-alternatives></ref><ref id="cit26"><label>26</label><citation-alternatives><mixed-citation xml:lang="ru">Manuello J, Nani A, Premi E, Borroni B, Costa T, Tatu K et al. The Pathoconnectivity Profile of Alzheimer’s Disease: A Morphometric Coalteration Network Analysis. Front Neurol. 2018;8:739. https://doi.org/10.3389/fneur.2017.00739.</mixed-citation><mixed-citation xml:lang="en">Manuello J, Nani A, Premi E, Borroni B, Costa T, Tatu K et al. The Pathoconnectivity Profile of Alzheimer’s Disease: A Morphometric Coalteration Network Analysis. Front Neurol. 2018;8:739. https://doi.org/10.3389/fneur.2017.00739.</mixed-citation></citation-alternatives></ref><ref id="cit27"><label>27</label><citation-alternatives><mixed-citation xml:lang="ru">Schuff N, Woerner N, Boreta L, Kornfield T, Shaw LM, Trojanowski JQ et al. MRI of hippocampal volume loss in early Alzheimer’s disease in relation to ApoE genotype and biomarkers. Brain. 2009;132(4):1067–1077. https://doi.org/10.1093/brain/awp007.</mixed-citation><mixed-citation xml:lang="en">Schuff N, Woerner N, Boreta L, Kornfield T, Shaw LM, Trojanowski JQ et al. MRI of hippocampal volume loss in early Alzheimer’s disease in relation to ApoE genotype and biomarkers. Brain. 2009;132(4):1067–1077. https://doi.org/10.1093/brain/awp007.</mixed-citation></citation-alternatives></ref><ref id="cit28"><label>28</label><citation-alternatives><mixed-citation xml:lang="ru">Morra JH, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK et al. Validation of a fully automated 3D hippocampal segmentation method using subjects with Alzheimer’s disease mild cognitive impairment, and elderly controls. Neuroimage. 2008;43(1):59–68. https://doi.org/10.1016/j.neuroimage.2008.07.003.</mixed-citation><mixed-citation xml:lang="en">Morra JH, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK et al. Validation of a fully automated 3D hippocampal segmentation method using subjects with Alzheimer’s disease mild cognitive impairment, and elderly controls. Neuroimage. 2008;43(1):59–68. https://doi.org/10.1016/j.neuroimage.2008.07.003.</mixed-citation></citation-alternatives></ref><ref id="cit29"><label>29</label><citation-alternatives><mixed-citation xml:lang="ru">Jack CRJr, Petersen RC, Xu YC, Waring SC, O’Brien PC, Tangalos EG et al. Medial temporal atrophy on MRI in normal aging and very mild Alzheimer’s disease. Neurology. 1997;49(3):786–794. https://doi.org/10.1212/wnl.49.3.786.</mixed-citation><mixed-citation xml:lang="en">Jack CRJr, Petersen RC, Xu YC, Waring SC, O’Brien PC, Tangalos EG et al. Medial temporal atrophy on MRI in normal aging and very mild Alzheimer’s disease. Neurology. 1997;49(3):786–794. https://doi.org/10.1212/wnl.49.3.786.</mixed-citation></citation-alternatives></ref><ref id="cit30"><label>30</label><citation-alternatives><mixed-citation xml:lang="ru">Galton CJ, Gomez-Anson B, Antoun N, Scheltens P, Patterson K, Graves M et al. Temporal lobe rating scale: application to Alzheimer’s disease and frontotemporal dementia. J Neurol Neurosurg Psychiatry. 2001;70(2):165–173. https://doi.org/10.1136/jnnp.70.2.165.</mixed-citation><mixed-citation xml:lang="en">Galton CJ, Gomez-Anson B, Antoun N, Scheltens P, Patterson K, Graves M et al. Temporal lobe rating scale: application to Alzheimer’s disease and frontotemporal dementia. J Neurol Neurosurg Psychiatry. 2001;70(2):165–173. https://doi.org/10.1136/jnnp.70.2.165.</mixed-citation></citation-alternatives></ref><ref id="cit31"><label>31</label><citation-alternatives><mixed-citation xml:lang="ru">Chupin M, Mukuna-Bantumbakulu AR, Hasboun D, Bardinet E, Baillet S, Kinkingnéhun S et al. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer’s disease. Neuroimage. 2007;34(3):996–1019. https://doi.org/10.1016/j.neuroimage.2006.10.035.</mixed-citation><mixed-citation xml:lang="en">Chupin M, Mukuna-Bantumbakulu AR, Hasboun D, Bardinet E, Baillet S, Kinkingnéhun S et al. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer’s disease. Neuroimage. 2007;34(3):996–1019. https://doi.org/10.1016/j.neuroimage.2006.10.035.</mixed-citation></citation-alternatives></ref><ref id="cit32"><label>32</label><citation-alternatives><mixed-citation xml:lang="ru">Colliot O, Chételat G, Chupin M, Desgranges B, Magnin B, Benali H et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology. 2008;248(1):194–201. https://doi.org/10.1148/radiol.2481070876.</mixed-citation><mixed-citation xml:lang="en">Colliot O, Chételat G, Chupin M, Desgranges B, Magnin B, Benali H et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology. 2008;248(1):194–201. https://doi.org/10.1148/radiol.2481070876.</mixed-citation></citation-alternatives></ref><ref id="cit33"><label>33</label><citation-alternatives><mixed-citation xml:lang="ru">Wu J, Shahid SS, Lin Q, Hone-Blanchet A, Smith JL, Risk BB et al. Multimodal magnetic resonance imaging reveals distinct sensitivity of hippocampal subfields in asymptomatic stage of Alzheimer’s disease. Front Aging Neurosci. 2022;14:901140. https://doi.org/10.3389/fnagi.2022.901140.</mixed-citation><mixed-citation xml:lang="en">Wu J, Shahid SS, Lin Q, Hone-Blanchet A, Smith JL, Risk BB et al. Multimodal magnetic resonance imaging reveals distinct sensitivity of hippocampal subfields in asymptomatic stage of Alzheimer’s disease. Front Aging Neurosci. 2022;14:901140. https://doi.org/10.3389/fnagi.2022.901140.</mixed-citation></citation-alternatives></ref><ref id="cit34"><label>34</label><citation-alternatives><mixed-citation xml:lang="ru">Juottonen K, Laakso MP, Partanen K, Soininen H. Comparative MR analysis of the entorhinal cortex and hippocampus in diagnosing Alzheimer disease. AJNR Am J Neuroradiol. 1999;20(1):139–144. Available at: https://pubmed.ncbi.nlm.nih.gov/9974069.</mixed-citation><mixed-citation xml:lang="en">Juottonen K, Laakso MP, Partanen K, Soininen H. Comparative MR analysis of the entorhinal cortex and hippocampus in diagnosing Alzheimer disease. AJNR Am J Neuroradiol. 1999;20(1):139–144. Available at: https://pubmed.ncbi.nlm.nih.gov/9974069.</mixed-citation></citation-alternatives></ref><ref id="cit35"><label>35</label><citation-alternatives><mixed-citation xml:lang="ru">Pennanen C, Kivipelto M, Tuomainen S, Hartikainen P, Hänninen T, Laakso MP et al. Hippocampus and entorhinal cortex in mild cognitive impairment and early AD. Neurobiol Aging. 2004;25(3):303–310. https://doi.org/10.1016/S0197-4580(03)00084-8.</mixed-citation><mixed-citation xml:lang="en">Pennanen C, Kivipelto M, Tuomainen S, Hartikainen P, Hänninen T, Laakso MP et al. Hippocampus and entorhinal cortex in mild cognitive impairment and early AD. Neurobiol Aging. 2004;25(3):303–310. https://doi.org/10.1016/S0197-4580(03)00084-8.</mixed-citation></citation-alternatives></ref><ref id="cit36"><label>36</label><citation-alternatives><mixed-citation xml:lang="ru">Desikan RS, Cabral HJ, Hess CP, Dillon WP, Glastonbury CM, Weiner MW et al. Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer’s disease. Brain. 2009;132(8):2048–2057. https://doi.org/10.1093/brain/awp123.</mixed-citation><mixed-citation xml:lang="en">Desikan RS, Cabral HJ, Hess CP, Dillon WP, Glastonbury CM, Weiner MW et al. Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer’s disease. Brain. 2009;132(8):2048–2057. https://doi.org/10.1093/brain/awp123.</mixed-citation></citation-alternatives></ref><ref id="cit37"><label>37</label><citation-alternatives><mixed-citation xml:lang="ru">Marcus DS, Fotenos AF, Csernansky JG, Morris JC, Buckner RL. Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. J Cogn Neurosci. 2010;22(12):2677–2684. https://doi.org/10.1162/jocn.2009.21407.</mixed-citation><mixed-citation xml:lang="en">Marcus DS, Fotenos AF, Csernansky JG, Morris JC, Buckner RL. Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. J Cogn Neurosci. 2010;22(12):2677–2684. https://doi.org/10.1162/jocn.2009.21407.</mixed-citation></citation-alternatives></ref><ref id="cit38"><label>38</label><citation-alternatives><mixed-citation xml:lang="ru">Mosconi L, Tsui WH, Herholz K, Pupi A, Drzezga A, Lucignani G et al. Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias. J Nucl Med. 2008;49(3):390–398. https://doi.org/10.2967/jnumed.107.045385.</mixed-citation><mixed-citation xml:lang="en">Mosconi L, Tsui WH, Herholz K, Pupi A, Drzezga A, Lucignani G et al. Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias. J Nucl Med. 2008;49(3):390–398. https://doi.org/10.2967/jnumed.107.045385.</mixed-citation></citation-alternatives></ref><ref id="cit39"><label>39</label><citation-alternatives><mixed-citation xml:lang="ru">Jack CRJr, Lowe VJ, Senjem ML, Weigand SD, Kemp BJ, Shiung MM et al. 11C PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment. Brain. 2008;131(3):665–680. https://doi.org/10.1093/brain/awm336.</mixed-citation><mixed-citation xml:lang="en">Jack CRJr, Lowe VJ, Senjem ML, Weigand SD, Kemp BJ, Shiung MM et al. 11C PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment. Brain. 2008;131(3):665–680. https://doi.org/10.1093/brain/awm336.</mixed-citation></citation-alternatives></ref><ref id="cit40"><label>40</label><citation-alternatives><mixed-citation xml:lang="ru">Li Y, Rinne JO, Mosconi L, Pirraglia E, Rusinek H, DeSanti S et al. Regional analysis of FDG and PIB-PET images in normal aging, mild cognitive impairment, and Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2008;35(12):2169–2181. https://doi.org/10.1007/s00259-008-0833-y.</mixed-citation><mixed-citation xml:lang="en">Li Y, Rinne JO, Mosconi L, Pirraglia E, Rusinek H, DeSanti S et al. Regional analysis of FDG and PIB-PET images in normal aging, mild cognitive impairment, and Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2008;35(12):2169–2181. https://doi.org/10.1007/s00259-008-0833-y.</mixed-citation></citation-alternatives></ref><ref id="cit41"><label>41</label><citation-alternatives><mixed-citation xml:lang="ru">Small GW, Kepe V, Ercoli LM, Siddarth P, Bookheimer SY, Miller KJ et al. PET of brain amyloid and tau in mild cognitive impairment. N Engl J Med. 2006;355(25):2652–2663. https://doi.org/10.1056/NEJMoa054625.</mixed-citation><mixed-citation xml:lang="en">Small GW, Kepe V, Ercoli LM, Siddarth P, Bookheimer SY, Miller KJ et al. PET of brain amyloid and tau in mild cognitive impairment. N Engl J Med. 2006;355(25):2652–2663. https://doi.org/10.1056/NEJMoa054625.</mixed-citation></citation-alternatives></ref><ref id="cit42"><label>42</label><citation-alternatives><mixed-citation xml:lang="ru">Killiany RJ, Gomez-Isla T, Moss M, Kikinis R, Sandor T, Jolesz F et al. Use of structural magnetic resonance imaging to predict who will get Alzheimer’s disease. Ann Neurol. 2000;47(4):430–439. Available at: https://pubmed.ncbi.nlm.nih.gov/10762153.</mixed-citation><mixed-citation xml:lang="en">Killiany RJ, Gomez-Isla T, Moss M, Kikinis R, Sandor T, Jolesz F et al. Use of structural magnetic resonance imaging to predict who will get Alzheimer’s disease. Ann Neurol. 2000;47(4):430–439. Available at: https://pubmed.ncbi.nlm.nih.gov/10762153.</mixed-citation></citation-alternatives></ref><ref id="cit43"><label>43</label><citation-alternatives><mixed-citation xml:lang="ru">Davatzikos C, Fan Y, Wu X, Shen D, Resnick SM. Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobiol Aging. 2008;29(4):514–523. https://doi.org/10.1016/j.neurobiolaging.2006.11.010.</mixed-citation><mixed-citation xml:lang="en">Davatzikos C, Fan Y, Wu X, Shen D, Resnick SM. Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobiol Aging. 2008;29(4):514–523. https://doi.org/10.1016/j.neurobiolaging.2006.11.010.</mixed-citation></citation-alternatives></ref><ref id="cit44"><label>44</label><citation-alternatives><mixed-citation xml:lang="ru">Kwak K, Niethammer M, Giovanello KS, Styner M, Dayan E.Differential Role for Hippocampal Subfields in Alzheimer’s Disease Progression Revealed with Deep Learning. Cereb Cortex. 2022;32(3):467–478. https://doi.org/10.1093/cercor/bhab223.</mixed-citation><mixed-citation xml:lang="en">Kwak K, Niethammer M, Giovanello KS, Styner M, Dayan E.Differential Role for Hippocampal Subfields in Alzheimer’s Disease Progression Revealed with Deep Learning. Cereb Cortex. 2022;32(3):467–478. https://doi.org/10.1093/cercor/bhab223.</mixed-citation></citation-alternatives></ref><ref id="cit45"><label>45</label><citation-alternatives><mixed-citation xml:lang="ru">Lerch JP, Pruessner J, Zijdenbos AP, Collins DL, Teipel SJ, Hampel H, Evans AC. Automated cortical thickness measurements from MRI can accurately separate Alzheimer’s patients from normal elderly controls. Neurobiol Aging. 2008;29(1):23–30. https://doi.org/10.1016/j.neurobiolaging.2006.09.013.</mixed-citation><mixed-citation xml:lang="en">Lerch JP, Pruessner J, Zijdenbos AP, Collins DL, Teipel SJ, Hampel H, Evans AC. Automated cortical thickness measurements from MRI can accurately separate Alzheimer’s patients from normal elderly controls. Neurobiol Aging. 2008;29(1):23–30. https://doi.org/10.1016/j.neurobiolaging.2006.09.013.</mixed-citation></citation-alternatives></ref><ref id="cit46"><label>46</label><citation-alternatives><mixed-citation xml:lang="ru">Wu Z, Peng Y, Hong M, Zhang Y. Gray Matter Deterioration Pattern During Alzheimer’s Disease Progression: A Regions-of-Interest Based Surface Morphometry Study. Front Aging Neurosci. 2021;13:593898. https://doi.org/10.3389/fnagi.2021.593898.</mixed-citation><mixed-citation xml:lang="en">Wu Z, Peng Y, Hong M, Zhang Y. Gray Matter Deterioration Pattern During Alzheimer’s Disease Progression: A Regions-of-Interest Based Surface Morphometry Study. Front Aging Neurosci. 2021;13:593898. https://doi.org/10.3389/fnagi.2021.593898.</mixed-citation></citation-alternatives></ref><ref id="cit47"><label>47</label><citation-alternatives><mixed-citation xml:lang="ru">Gerardin E, Chételat G, Chupin M, Cuingnet R, Desgranges B, Kim HS et al. Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage. 2009;47(4):1476–1486. https://doi.org/10.1016/j.neuroimage.2009.05.036.</mixed-citation><mixed-citation xml:lang="en">Gerardin E, Chételat G, Chupin M, Cuingnet R, Desgranges B, Kim HS et al. Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage. 2009;47(4):1476–1486. https://doi.org/10.1016/j.neuroimage.2009.05.036.</mixed-citation></citation-alternatives></ref><ref id="cit48"><label>48</label><citation-alternatives><mixed-citation xml:lang="ru">Costafreda SG, Dinov ID, Tu Z, Shi Y, Liu CY, Kloszewska I et al. Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment. Neuroimage. 2011;56(1):212–219. https://doi.org/10.1016/j.neuroimage.2011.01.050.</mixed-citation><mixed-citation xml:lang="en">Costafreda SG, Dinov ID, Tu Z, Shi Y, Liu CY, Kloszewska I et al. Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment. Neuroimage. 2011;56(1):212–219. https://doi.org/10.1016/j.neuroimage.2011.01.050.</mixed-citation></citation-alternatives></ref><ref id="cit49"><label>49</label><citation-alternatives><mixed-citation xml:lang="ru">Plant C, Teipel SJ, Oswald A, Böhm C, Meindl T, Mourao-Miranda J et al. Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. Neuroimage. 2010;50(1):162–174. https://doi.org/10.1016/j.neuroimage.2009.11.046.</mixed-citation><mixed-citation xml:lang="en">Plant C, Teipel SJ, Oswald A, Böhm C, Meindl T, Mourao-Miranda J et al. Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. Neuroimage. 2010;50(1):162–174. https://doi.org/10.1016/j.neuroimage.2009.11.046.</mixed-citation></citation-alternatives></ref><ref id="cit50"><label>50</label><citation-alternatives><mixed-citation xml:lang="ru">Misra C, Fan Y, Davatzikos C. Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage. 2009;44(4):1415–1422. https://doi.org/10.1016/j.neuroimage.2008.10.031.</mixed-citation><mixed-citation xml:lang="en">Misra C, Fan Y, Davatzikos C. Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage. 2009;44(4):1415–1422. https://doi.org/10.1016/j.neuroimage.2008.10.031.</mixed-citation></citation-alternatives></ref><ref id="cit51"><label>51</label><citation-alternatives><mixed-citation xml:lang="ru">Duchesne S, Bocti C, De Sousa K, Frisoni GB, Chertkow H, Collins DL. Amnestic MCI future clinical status prediction using baseline MRI features. Neurobiol Aging. 2010;31(9):1606–1617. https://doi.org/10.1016/j.neurobiolaging.2008.09.003.</mixed-citation><mixed-citation xml:lang="en">Duchesne S, Bocti C, De Sousa K, Frisoni GB, Chertkow H, Collins DL. Amnestic MCI future clinical status prediction using baseline MRI features. Neurobiol Aging. 2010;31(9):1606–1617. https://doi.org/10.1016/j.neurobiolaging.2008.09.003.</mixed-citation></citation-alternatives></ref><ref id="cit52"><label>52</label><citation-alternatives><mixed-citation xml:lang="ru">Csernansky JG, Wang L, Swank J, Miller JP, Gado M, McKeel D et al. Preclinical detection of Alzheimer’s disease: hippocampal shape and volume predict dementia onset in the elderly. Neuroimage. 2005;25(3):783–792. https://doi.org/10.1016/j.neuroimage.2004.12.036.</mixed-citation><mixed-citation xml:lang="en">Csernansky JG, Wang L, Swank J, Miller JP, Gado M, McKeel D et al. Preclinical detection of Alzheimer’s disease: hippocampal shape and volume predict dementia onset in the elderly. Neuroimage. 2005;25(3):783–792. https://doi.org/10.1016/j.neuroimage.2004.12.036.</mixed-citation></citation-alternatives></ref><ref id="cit53"><label>53</label><citation-alternatives><mixed-citation xml:lang="ru">Apostolova LG, Dutton RA, Dinov ID, Hayashi KM, Toga AW, Cummings JL, Thompson PM. Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps. Arch Neurol. 2006;63(5):693–699. https://doi.org/10.1001/archneur.63.5.693.</mixed-citation><mixed-citation xml:lang="en">Apostolova LG, Dutton RA, Dinov ID, Hayashi KM, Toga AW, Cummings JL, Thompson PM. Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps. Arch Neurol. 2006;63(5):693–699. https://doi.org/10.1001/archneur.63.5.693.</mixed-citation></citation-alternatives></ref><ref id="cit54"><label>54</label><citation-alternatives><mixed-citation xml:lang="ru">Morra JH, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK et al. Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls. Neuroimage. 2009;45(1):3–15. https://doi.org/10.1016/j.neuroimage.2008.10.043.</mixed-citation><mixed-citation xml:lang="en">Morra JH, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK et al. Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls. Neuroimage. 2009;45(1):3–15. https://doi.org/10.1016/j.neuroimage.2008.10.043.</mixed-citation></citation-alternatives></ref><ref id="cit55"><label>55</label><citation-alternatives><mixed-citation xml:lang="ru">Dickerson BC, Goncharova I, Sullivan MP, Forchetti C, Wilson RS, Bennett DA et al. MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer’s disease. Neurobiol Aging. 2001;22(5):747–754. https://doi.org/10.1016/s0197-4580(01)00271-8.</mixed-citation><mixed-citation xml:lang="en">Dickerson BC, Goncharova I, Sullivan MP, Forchetti C, Wilson RS, Bennett DA et al. MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer’s disease. Neurobiol Aging. 2001;22(5):747–754. https://doi.org/10.1016/s0197-4580(01)00271-8.</mixed-citation></citation-alternatives></ref><ref id="cit56"><label>56</label><citation-alternatives><mixed-citation xml:lang="ru">Jack CRJr, Petersen RC, Xu YC, O’Brien PC, Smith GE, Ivnik RJ et al. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology. 1999;52(7):1397–1403. https://doi.org/10.1212/wnl.52.7.1397.</mixed-citation><mixed-citation xml:lang="en">Jack CRJr, Petersen RC, Xu YC, O’Brien PC, Smith GE, Ivnik RJ et al. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology. 1999;52(7):1397–1403. https://doi.org/10.1212/wnl.52.7.1397.</mixed-citation></citation-alternatives></ref><ref id="cit57"><label>57</label><citation-alternatives><mixed-citation xml:lang="ru">Killiany RJ, Hyman BT, Gomez-Isla T, Moss MB, Kikinis R, Jolesz F et al. MRI measures of entorhinal cortex vs hippocampus in preclinical AD. Neurology. 2002;58(8):1188–1196. https://doi.org/10.1212/wnl.58.8.1188.</mixed-citation><mixed-citation xml:lang="en">Killiany RJ, Hyman BT, Gomez-Isla T, Moss MB, Kikinis R, Jolesz F et al. MRI measures of entorhinal cortex vs hippocampus in preclinical AD. Neurology. 2002;58(8):1188–1196. https://doi.org/10.1212/wnl.58.8.1188.</mixed-citation></citation-alternatives></ref><ref id="cit58"><label>58</label><citation-alternatives><mixed-citation xml:lang="ru">deToledo-Morrell L, Stoub TR, Bulgakova M, Wilson RS, Bennett DA, Leurgans S et al. MRI-derived entorhinal volume is a good predictor of conversion from MCI to AD. Neurobiol Aging. 2004;25(9):1197–1203. https://doi.org/10.1016/j.neurobiolaging.2003.12.007.</mixed-citation><mixed-citation xml:lang="en">deToledo-Morrell L, Stoub TR, Bulgakova M, Wilson RS, Bennett DA, Leurgans S et al. MRI-derived entorhinal volume is a good predictor of conversion from MCI to AD. Neurobiol Aging. 2004;25(9):1197–1203. https://doi.org/10.1016/j.neurobiolaging.2003.12.007.</mixed-citation></citation-alternatives></ref><ref id="cit59"><label>59</label><citation-alternatives><mixed-citation xml:lang="ru">Tapiola T, Pennanen C, Tapiola M, Tervo S, Kivipelto M, Hänninen T et al. MRI of hippocampus and entorhinal cortex in mild cognitive impairment: a follow-up study. Neurobiol Aging. 2008;29(1):31–38. https://doi.org/10.1016/j.neurobiolaging.2006.09.007.</mixed-citation><mixed-citation xml:lang="en">Tapiola T, Pennanen C, Tapiola M, Tervo S, Kivipelto M, Hänninen T et al. MRI of hippocampus and entorhinal cortex in mild cognitive impairment: a follow-up study. Neurobiol Aging. 2008;29(1):31–38. https://doi.org/10.1016/j.neurobiolaging.2006.09.007.</mixed-citation></citation-alternatives></ref><ref id="cit60"><label>60</label><citation-alternatives><mixed-citation xml:lang="ru">Gómez-Isla T, Hollister R, West H, Mui S, Growdon JH, Petersen RC et al. Neuronal loss correlates with but exceeds neurofibrillary tangles in Alzheimer’s disease. Ann Neurol. 1997;41(1):17–24. https://doi.org/10.1002/ana.410410106.</mixed-citation><mixed-citation xml:lang="en">Gómez-Isla T, Hollister R, West H, Mui S, Growdon JH, Petersen RC et al. Neuronal loss correlates with but exceeds neurofibrillary tangles in Alzheimer’s disease. Ann Neurol. 1997;41(1):17–24. https://doi.org/10.1002/ana.410410106.</mixed-citation></citation-alternatives></ref><ref id="cit61"><label>61</label><citation-alternatives><mixed-citation xml:lang="ru">Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO et al. Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 2011;56(2):766–781. https://doi.org/10.1016/j.neuroimage.2010.06.013.</mixed-citation><mixed-citation xml:lang="en">Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO et al. Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 2011;56(2):766–781. https://doi.org/10.1016/j.neuroimage.2010.06.013.</mixed-citation></citation-alternatives></ref><ref id="cit62"><label>62</label><citation-alternatives><mixed-citation xml:lang="ru">Wolz R, Julkunen V, Koikkalainen J, Niskanen E, Zhang DP, Rueckert D et al. Multimethod analysis of MRI images in early diagnostics of Alzheimer’s disease. PLoS ONE. 2011;6(10):e25446. https://doi.org/10.1371/journal.pone.0025446.</mixed-citation><mixed-citation xml:lang="en">Wolz R, Julkunen V, Koikkalainen J, Niskanen E, Zhang DP, Rueckert D et al. Multimethod analysis of MRI images in early diagnostics of Alzheimer’s disease. PLoS ONE. 2011;6(10):e25446. https://doi.org/10.1371/journal.pone.0025446.</mixed-citation></citation-alternatives></ref><ref id="cit63"><label>63</label><citation-alternatives><mixed-citation xml:lang="ru">Desikan RS, Cabral HJ, Settecase F, Hess CP, Dillon WP, Glastonbury CM et al. Automated MRI measures predict progression to Alzheimer’s disease. Neurobiol Aging. 2010;31(8):1364–1374. https://doi.org/10.1016/j.neurobiolaging.2010.04.023.</mixed-citation><mixed-citation xml:lang="en">Desikan RS, Cabral HJ, Settecase F, Hess CP, Dillon WP, Glastonbury CM et al. Automated MRI measures predict progression to Alzheimer’s disease. Neurobiol Aging. 2010;31(8):1364–1374. https://doi.org/10.1016/j.neurobiolaging.2010.04.023.</mixed-citation></citation-alternatives></ref><ref id="cit64"><label>64</label><citation-alternatives><mixed-citation xml:lang="ru">Vemuri P, Wiste HJ, Weigand SD, Shaw LM, Trojanowski JQ, Weiner MW et al. MRI and CSF biomarkers in normal, MCI, and AD subjects: predicting future clinical change. Neurology. 2009;73(4):294–301. https://doi.org/10.1212/WNL.0b013e3181af79fb.</mixed-citation><mixed-citation xml:lang="en">Vemuri P, Wiste HJ, Weigand SD, Shaw LM, Trojanowski JQ, Weiner MW et al. MRI and CSF biomarkers in normal, MCI, and AD subjects: predicting future clinical change. Neurology. 2009;73(4):294–301. https://doi.org/10.1212/WNL.0b013e3181af79fb.</mixed-citation></citation-alternatives></ref><ref id="cit65"><label>65</label><citation-alternatives><mixed-citation xml:lang="ru">Frisoni GB, Fox NC, Jack CRJr, Scheltens P, Thompson PM. The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol. 2010;6(2):67–77. https://doi.org/10.1038/nrneurol.2009.215.</mixed-citation><mixed-citation xml:lang="en">Frisoni GB, Fox NC, Jack CRJr, Scheltens P, Thompson PM. The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol. 2010;6(2):67–77. https://doi.org/10.1038/nrneurol.2009.215.</mixed-citation></citation-alternatives></ref><ref id="cit66"><label>66</label><citation-alternatives><mixed-citation xml:lang="ru">Bracoud L, Bouguen E, Bonneville F, Schaerer J, Kiyasova V, Roche F et al. DWI and DTI results on normal controls MCI and Alzheimer’s disease subjects from the rosas study. J Alzheimers Assoc. 2015;11(7):801–802. https://doi.org/10.1016/j.jalz.2015.06.129.</mixed-citation><mixed-citation xml:lang="en">Bracoud L, Bouguen E, Bonneville F, Schaerer J, Kiyasova V, Roche F et al. DWI and DTI results on normal controls MCI and Alzheimer’s disease subjects from the rosas study. J Alzheimers Assoc. 2015;11(7):801–802. https://doi.org/10.1016/j.jalz.2015.06.129.</mixed-citation></citation-alternatives></ref><ref id="cit67"><label>67</label><citation-alternatives><mixed-citation xml:lang="ru">LeCun Y, Bengio Y, Hinton G.Deep learning. Nature. 2015;521(7553):436–444. https://doi.org/10.1038/nature14539.</mixed-citation><mixed-citation xml:lang="en">LeCun Y, Bengio Y, Hinton G.Deep learning. Nature. 2015;521(7553):436–444. https://doi.org/10.1038/nature14539.</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
