Share：
Share this content in WeChat
X
[Chinese][PDF]7842
• Review •
Application value of multi-modal imaging technique in early diagnosis of Alzheimer's disease
LI Qing  YUE Xipeng  BAI Yan  LUO Yu  WANG Meiyun 

Cite this article as: Li Q, Yue XP, Bai Y, et al. Application value of multi-modal imaging technique in early diagnosis of Alzheimer's disease[J]. Chin J Magn Reson Imaging, 2022, 13(11): 110-114. DOI:10.12015/issn.1674-8034.2022.11.021.


[Abstract] Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive impairment and memory impairment, and it is also a great challenge in the process of population aging in China. Intervention in the early stage of AD is of great significance to delay the progress of the disease and improve the prognosis. Therefore, the early diagnosis of AD is very important. Multimodal imaging technology provides important imaging evidence for the pathogenesis and early clinical diagnosis of AD from the aspects of structure, function, metabolism and so on. We reviewed multimodal imaging technology, including MRI and positron emission computed tomography (PET) technology such as structure and function, and analyzed the application value of reflecting different brain characteristics in early diagnosis of AD patients in this article, in order to reveal the early pathological changes of AD from the perspective of imaging, improve the efficiency of early diagnosis of AD, and guide clinical treatment in the future.
[Keywords] Alzheimer's disease；mild cognitive impairment；multimodal imaging technology；early diagnosis；magnetic resonance imaging；functional magnetic resonance imaging；positron emission computed tomography

LI Qing1, 2   YUE Xipeng2, 3   BAI Yan2   LUO Yu2, 3   WANG Meiyun2, 3*  

1 Xinxiang Medical University, Xinxiang 453003, China

2 Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou 450003, China

3 Department of Medical Imaging, Zhengzhou University People's Hospital, Zhengzhou 450003, China

Wang MY, E-mail: mywang@ha.edu.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81720108021); Medical Science and Technology Research Project of Henan Province (No. SBGJ202101002).
Received  2022-07-29
Accepted  2022-10-13
DOI: 10.12015/issn.1674-8034.2022.11.021
Cite this article as: Li Q, Yue XP, Bai Y, et al. Application value of multi-modal imaging technique in early diagnosis of Alzheimer's disease[J]. Chin J Magn Reson Imaging, 2022, 13(11): 110-114. DOI:10.12015/issn.1674-8034.2022.11.021.

[1]
Scheef L, Spottke A, Daerr M, et al. Glucose metabolism, gray matter structure, and memory decline in subjective memory impairment[J]. Neurology, 2012, 79(13): 1332-1339. DOI: 10.1212/WNL.0b013e31826c1a8d.
[2]
Sperling RA, Aisen PS, Beckett LA, et al. Toward defining the preclinical stages of Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease[J]. Alzheimer's & dementia, 2011, 7(3): 280-292. DOI: 10.1016/j.jalz.2011.03.003.
[3]
Lai ZY, Zhang QP, Lai YQ, et al. MRI research progress of subjective cognitive decline[J]. Chin J Magn Reson Imaging, 2022, 13(3): 126-128, 142. DOI: 10.12015/issn.1674-8034.2022.03.031.
[4]
Davis M, Connell T O, Johnson S, et al. Estimating Alzheimer's disease progression rates from normal cognition through mild cognitive impairment and stages of dementia[J]. Curr Alzheimer Res, 2018, 15(8): 777-788. DOI: 10.2174/1567205015666180119092427.
[5]
Jack CR, Bennett DA, Blennow K, et al. A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers[J]. Neurology, 2016, 87(5): 539-547. DOI: 10.1212/WNL.0000000000002923.
[6]
Chandra A, Dervenoulas G, Politis M. Magnetic resonance imaging in Alzheimer's disease and mild cognitive impairment[J]. J Neurol, 2018, 266: 1293-1302. DOI: 10.1007/s00415-018-9016-3.
[7]
Li XF, Coyle D, Maguire L, et al. Gray matter concentration and effective connectivity changes in Alzheimer's disease: a longitudinal structural MRI study[J]. Neuroradiology, 2011, 53(10): 733-748. DOI: 10.1007/s00234-010-0795-1.
[8]
Cavedo E, Boccardi M, Ganzola R, et al. Local amygdala structural differences with 3T MRI in patients with Alzheimer disease[J]. Neurology, 2011, 76(8): 727-733. DOI: 10.1212/wnl.0b013e31820d62d9.
[9]
Jobin B, Boller B, Frasnelli J. Volumetry of olfactory structures in mild cognitive impairment and Alzheimer's disease: a systematic review and a meta-analysis[J/OL]. Brain Sci, 2021, 11(8) [2022-07-28]. https://www.mdpi.com/2076-3425/11/8/1010. DOI: 10.3390/brainsci11081010.
[10]
McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease[J]. Alzheimers Dement, 2011, 7(3): 263-269. DOI: 10.1016/j.jalz.2011.03.005.
[11]
Bonner-Jackson A, Mahmoud S, Miller J, et al. Verbal and non-verbal memory and hippocampal volumes in a memory clinic population[J/OL]. Alzheimers Res Ther, 2015, 7(1) [2022-07-28]. https://alzres.biomedcentral.com/articles/10.1186/s13195-015-0147-9. DOI: 10.1186/s13195-015-0147-9.
[12]
Allebone J, Kanaan R, Maller J, et al. Bilateral volume reduction in posterior hippocampus in psychosis of epilepsy[J]. J Neurol Neurosurg Psychiatry, 2019, 90(6): 688-694. DOI: 10.1136/jnnp-2018-319396.
[13]
Achterberg HC, van der Lijn F, den Heijer T, et al. Hippocampal shape is predictive for the development of dementia in a normal, elderly population[J]. Hum Brain Mapp, 2014, 35(5): 2359-2371. DOI: 10.1002/hbm.22333.
[14]
West MJ, Kawas CH, Stewart WF, et al. Hippocampal neurons in pre-clinical Alzheimer's disease[J]. Neurobiol Aging, 2004, 25(9): 1205-1212. DOI: 10.1016/j.neurobiolaging.2003.12.005.
[15]
Ryu SY, Lim EY, Na S, et al. Hippocampal and entorhinal structures in subjective memory impairment: a combined MRI volumetric and DTI study[J]. Int Psychogeriatr, 2017, 29(5): 785-792. DOI: 10.1017/S1041610216002349.
[16]
Guo XY, Chang Y, Kim Y, et al. Development and evaluation of a T1 standard brain template for Alzheimer disease[J]. Quant Imaging Med Surg, 2021, 11(6): 2224-2244. DOI: 10.21037/qims-20-710.
[17]
Basaia S, Agosta F, Wagner L, et al. Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks[J/OL]. Neuroimage Clin, 2019 [2022-07-28]. https://linkinghub.elsevier.com/retrieve/pii/S2213-1582(18)30393-0. DOI: 10.1016/j.nicl.2018.101645.
[18]
Poloni KM, Ferrari RJ. Automated detection, selection and classification of hippocampal landmark points for the diagnosis of Alzheimer's disease[J/OL]. Comput Methods Programs Biomed, 2022 [2022-07-28]. https://www.sciencedirect.com/science/article/abs/pii/S0169260721006556. DOI: 10.1016/j.cmpb.2021.106581.
[19]
Giulietti G, Torso M, Serra L, et al. Whole brain white matter histogram analysis of diffusion tensor imaging data detects microstructural damage in mild cognitive impairment and Alzheimer's disease patients[J]. J Magn Reson Imaging, 2018, 48(3): 767-779. DOI: 10.1002/jmri.25947.
[20]
Qin LZ, Guo ZW, McClure MA, et al. White matter changes from mild cognitive impairment to Alzheimer's disease: a meta-analysis[J]. Acta Neurol Belg, 2021, 121(6): 1435-1447. DOI: 10.1007/s13760-020-01322-5.
[21]
Shafer AT, Williams OA, Perez E, et al. Accelerated decline in white matter microstructure in subsequently impaired older adults and its relationship with cognitive decline[J/OL]. Brain Commun, 2022 [2022-07-28]. https://www.medrxiv.org/content/10.1101/2020.09.23.20187450v1. DOI: 10.1093/braincomms/fcac051.
[22]
Bozoki AC, Korolev IO, Davis NC, et al. Disruption of limbic white matter pathways in mild cognitive impairment and Alzheimer's disease: a DTI/FDG-PET study[J]. Hum Brain Mapp, 2012, 33(8): 1792-1802. DOI: 10.1002/hbm.21320.
[23]
Huang H, Fan X, Weiner M, et al. Distinctive disruption patterns of white matter tracts in Alzheimer's disease with full diffusion tensor characterization[J]. Neurobiol Aging, 2012, 33(9): 2029-2045. DOI: 10.1016/j.neurobiolaging.2011.06.027.
[24]
Brueggen K, Dyrba M, Cardenas-Blanco A, et al. Structural integrity in subjective cognitive decline, mild cognitive impairment and Alzheimer's disease based on multicenter diffusion tensor imaging[J]. J Neurol, 2019, 266(10): 2465-2474. DOI: 10.1007/s00415-019-09429-3.
[25]
Binette AP, Theaud G, Rheault F, et al. Bundle-specific associations between white matter microstructure and Aβ and tau pathology in preclinical Alzheimer's disease[J/OL]. eLife, 2021 [2022-07-28]. https://elifesciences.org/articles/62929. DOI: 10.7554/eLife.62929.
[26]
Phillips OR, Joshi SH, Piras F, et al. The superficial white matter in Alzheimer's disease[J]. Hum Brain Mapp, 2016, 37(4): 1321-1334. DOI: 10.1002/hbm.23105.
[27]
Bigham B, Zamanpour SA, Zemorshidi F, et al. Identification of superficial white matter abnormalities in Alzheimer's disease and mild cognitive impairment using diffusion tensor imaging[J]. J Alzheimers Dis Rep, 2020, 4(1): 49-59. DOI: 10.3233/ADR-190149.
[28]
Flak MM, Hol HR, Hernes SS, et al. Cognitive profiles and atrophy ratings on MRI in senior patients with mild cognitive impairment[J/OL]. Front Aging Neurosci, 2018, 10 [2022-07-28]. https://www.frontiersin.org/articles/10.3389/fnagi.2018.00384/full. DOI: 10.3389/fnagi.2018.00384.
[29]
Bigham B, Zamanpour SA, Zare H, et al. Features of the superficial white matter as biomarkers for the detection of Alzheimer's disease and mild cognitive impairment: a diffusion tensor imaging study[J/OL]. Heliyon, 2022 [2022-07-28]. https://www.cell.com/heliyon/fulltext/S2405-8440(22)00013-5. DOI: 10.1016/j.heliyon.2022.e08725.
[30]
Liu L, Jiang H, Wang D, et al. A study of regional homogeneity of resting-state Functional Magnetic Resonance Imaging in mild cognitive impairment[J/OL]. Behav Brain Res, 2021 [2022-07-28]. https://www.sciencedirect.com/science/article/abs/pii/S0166432820308020. DOI: 10.1016/j.bbr.2020.113103.
[31]
Liu XN, Wang SQ, Zhang XQ, et al. Abnormal amplitude of low-frequency fluctuations of intrinsic brain activity in Alzheimer's disease[J]. J Alzheimers Dis, 2014, 40(2): 387-397. DOI: 10.3233/JAD-131322.
[32]
Talwar P, Kushwaha S, Chaturvedi M, et al. Systematic review of different neuroimaging correlates in mild cognitive impairment and Alzheimer's disease[J]. Clin Neuroradiol, 2021, 31(4): 953-967. DOI: 10.1007/s00062-021-01057-7.
[33]
Wang TL, Zhao XF, Wu Y, et al. The study on the combined regional homogeneity and functional connectivity of resting-state magnetic resonance in patients with subjective cognitive decline[J]. Chin J Geriatr, 2021, 40(1): 72-75. DOI: 10.3760/cma.j.issn.0254-9026.2021.01.010.
[34]
Koch W, Teipel S, Mueller S, et al. Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer's disease[J]. Neurobiol Aging, 2012, 33(3): 466-478. DOI: 10.1016/j.neurobiolaging.2010.04.013.
[35]
Zhou J, Seeley WW. Network dysfunction in Alzheimer's disease and frontotemporal dementia: implications for psychiatry[J]. Biol Psychiatry, 2014, 75(7): 565-573. DOI: 10.1016/j.biopsych.2014.01.020.
[36]
Su YY, Zhang XD, Schoepf UJ, et al. Lower functional connectivity of default mode network in cognitively normal young adults with mutation of APP, presenilins and APOE ε4[J]. Brain Imaging Behav, 2017, 11(3): 818-828. DOI: 10.1007/s11682-016-9556-z.
[37]
Jokar S, Khazaei S, Behnammanesh H, et al. Recent advances in the design and applications of amyloid-β peptide aggregation inhibitors for Alzheimer's disease therapy[J]. Biophys Rev, 2019, 11(6): 901-925. DOI: 10.1007/s12551-019-00606-2.
[38]
Fu ZN, Caprihan A, Chen JY, et al. Altered static and dynamic functional network connectivity in Alzheimer's disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities[J]. Hum Brain Mapp, 2019, 40(11): 3203-3221. DOI: 10.1002/hbm.24591.
[39]
Liang LY, Yuan YM, Wei YC, et al. Recurrent and concurrent patterns of regional BOLD dynamics and functional connectivity dynamics in cognitive decline[J/OL]. Alzheimers Res Ther, 2021, 13(1) [2022-07-28]. https://alzres.biomedcentral.com/articles/10.1186/s13195-020-00764-6. DOI: 10.1186/s13195-020-00764-6.
[40]
Wong D, Atiya S, Fogarty J, et al. Reduced hippocampal glutamate and posterior cingulate N-acetyl aspartate in mild cognitive impairment and Alzheimer's disease is associated with episodic memory performance and white matter integrity in the cingulum: a pilot study[J]. J Alzheimers Dis, 2020, 73(4): 1385-1405. DOI: 10.3233/JAD-190773.
[41]
Song T, Song XP, Zhu C, et al. Mitochondrial dysfunction, oxidative stress, neuroinflammation, and metabolic alterations in the progression of Alzheimer's disease: a meta-analysis of in vivo magnetic resonance spectroscopy studies[J/OL]. Ageing Res Rev, 2021 [2022-07-28]. https://www.sciencedirect.com/science/article/pii/S1568163721002506. DOI: 10.1016/j.arr.2021.101503.
[42]
Piersson AD, Mohamad M, Rajab F, et al. Cerebrospinal fluid amyloid beta, tau levels, apolipoprotein, and 1H-MRS brain metabolites in Alzheimer's disease: a systematic review[J]. Acad Radiol, 2021, 28(10): 1447-1463. DOI: 10.1016/j.acra.2020.06.006.
[43]
Chaney A, Williams SR, Boutin H. In vivo molecular imaging of neuroinflammation in Alzheimer's disease[J]. J Neurochem, 2019, 149(4): 438-451. DOI: 10.1111/jnc.14615.
[44]
Chen QY, Abrigo J, Liu WT, et al. Lower posterior cingulate N-acetylaspartate to creatine level in early detection of biologically defined Alzheimer's disease[J]. Brain Sci, 2022, 12(6) [2022-07-28]. https://www.mdpi.com/2076-3425/12/6/722. DOI: 10.3390/brainsci12060722.
[45]
Shukla D, Mandal PK, Mishra R, et al. Hippocampal glutathione depletion and pH increment in Alzheimer's disease: an in vivo MRS study[J]. J Alzheimers Dis, 2021, 84(3): 1139-1152. DOI: 10.3233/JAD-215032.
[46]
Sultana R, Piroddi M, Galli F, et al. Protein levels and activity of some antioxidant enzymes in hippocampus of subjects with amnestic mild cognitive impairment[J]. Neurochem Res, 2008, 33(12): 2540-2546. DOI: 10.1007/s11064-008-9593-0.
[47]
Marjańska M, McCarten JR, Hodges JS, et al. Distinctive neurochemistry in Alzheimer's disease via 7 T in vivo magnetic resonance spectroscopy[J]. J Alzheimers Dis, 2019, 68(2): 559-569. DOI: 10.3233/JAD-180861.
[48]
Jagust W, Gitcho A, Sun F, et al. Brain imaging evidence of preclinical Alzheimer's disease in normal aging[J]. Ann Neurol, 2006, 59(4): 673-681. DOI: 10.1002/ana.20799.
[49]
Kato T, Inui Y, Nakamura A, et al. Brain fluorodeoxyglucose (FDG) PET in dementia[J]. Ageing Res Rev, 2016, 30: 73-84. DOI: 10.1016/j.arr.2016.02.003.
[50]
Ewers M, Brendel M, Rizk-Jackson A, et al. Reduced FDG-PET brain metabolism and executive function predict clinical progression in elderly healthy subjects[J]. Neuroimage Clin, 2014, 4: 45-52. DOI: 10.1016/j.nicl.2013.10.018.
[51]
Rowe CC, Villemagne VL. Amyloid imaging with PET in early Alzheimer disease diagnosis[J]. Med Clin North Am, 2013, 97(3): 377-398. DOI: 10.1016/j.mcna.2012.12.017.
[52]
Devanand DP, Mikhno A, Pelton GH, et al. Pittsburgh compound B (11C-PIB) and fluorodeoxyglucose (18 F-FDG) PET in patients with Alzheimer disease, mild cognitive impairment, and healthy controls[J]. J Geriatr Psychiatry Neurol, 2010, 23(3): 185-198. DOI: 10.1177/0891988710363715.
[53]
Forsberg A, Engler H, Almkvist O, et al. PET imaging of amyloid deposition in patients with mild cognitive impairment[J]. Neurobiol Aging, 2008, 29(10): 1456-1465. DOI: 10.1016/j.neurobiolaging.2007.03.029.
[54]
Bao WQ, Jia HM, Finnema S, et al. PET imaging for early detection of Alzheimer's disease: from pathologic to physiologic biomarkers[J]. PET Clin, 2017, 12(3): 329-350. DOI: 10.1016/j.cpet.2017.03.001.
[55]
Betthauser TJ, Koscik RL, Jonaitis EM, et al. Amyloid and tau imaging biomarkers explain cognitive decline from late middle-age[J]. Brain, 2020, 143(1): 320-335. DOI: 10.1093/brain/awz378.
[56]
Pontecorvo MJ, Devous MD, Kennedy I, et al. A multicentre longitudinal study of flortaucipir (18F) in normal ageing, mild cognitive impairment and Alzheimer's disease dementia[J]. Brain, 2019, 142(6): 1723-1735. DOI: 10.1093/brain/awz090.
[57]
Hu YH, Wen CY, Cao GQ, et al. Brain network connectivity feature extraction using deep learning for Alzheimer's disease classification[J/OL]. Neurosci Lett, 2022 [2022-07-28]. https://www.sciencedirect.com/science/article/abs/pii/S0304394022002300. DOI: 10.1016/j.neulet.2022.136673.
[58]
Cope TE, Rittman T, Borchert RJ, et al. Tau burden and the functional connectome in Alzheimer's disease and progressive supranuclear palsy[J]. Brain, 2018, 141(2): 550-567. DOI: 10.1093/brain/awx347.
[59]
Khomenko YG, Kataeva GV, Bogdan AA, et al. Cerebral metabolism in patients with cognitive disorders: a combined MRS and PET study[J]. Zh Nevrol Psikhiatr Im S S Korsakova, 2019, 119(1): 51-58. DOI: 10.17116/jnevro201911901151.

PREV Anti NMDAR encephalitis with cortical laminar necrosis: One case report
NEXT Research progress of neurite orientation dispersion and density imaging in neurodegenerative diseases
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn