Journal of Diagnostics Concepts & Practice ›› 2022, Vol. 21 ›› Issue (01): 12-17.doi: 10.16150/j.1671-2870.2022.01.004
• Experts forum • Previous Articles Next Articles
Online:2022-02-25
Published:2022-02-25
CLC Number:
| [1] |
Dubois B, Feldman HH, Jacova C, et al. Research criteria for the diagnosis of Alzheimer′s disease: revising the NINCDS-ADRDA criteria[J]. Lancet Neurol, 2007, 6(8):734-746.
doi: 10.1016/S1474-4422(07)70178-3 pmid: 17616482 |
| [2] |
Dubois B, Feldman HH, Jacova C, et al. Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria[J]. Lancet Neurol, 2014, 13(6):614-629.
doi: 10.1016/S1474-4422(14)70090-0 pmid: 24849862 |
| [3] |
Jack CR Jr, Bennett DA, Blennow K, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer′s disease[J]. Alzheimers Dement, 2018, 14(4):535-562.
doi: 10.1016/j.jalz.2018.02.018 URL |
| [4] |
Pontecorvo MJ, Devous MD Sr, Navitsky M, et al. Relationships between flortaucipir PET tau binding and amyloid burden, clinical diagnosis, age and cognition[J]. Brain, 2017, 140(3):748-763.
doi: 10.1093/brain/aww334 pmid: 28077397 |
| [5] |
Marcone A, Garibotto V, Moresco RM, et al. [11C]-MP4A PET cholinergic measurements in amnestic mild cognitive impairment, probable Alzheimer′s disease, and dementia with Lewy bodies: a Bayesian method and voxel-based analysis[J]. J Alzheimers Dis, 2012, 31(2):387-399.
doi: 10.3233/JAD-2012-111748 URL |
| [6] |
Dubois B, Villain N, Frisoni GB, et al. Clinical diagnosis of Alzheimer′s disease: recommendations of the International Working Group[J]. Lancet Neurol, 2021, 20(6):484-496.
doi: 10.1016/S1474-4422(21)00066-1 pmid: 33933186 |
| [7] |
Shen L, Saykin AJ, Kim S, et al. Comparison of manual and automated determination of hippocampal volumes in MCI and early AD[J]. Brain Imaging Behav, 2010, 4(1):86-95.
doi: 10.1007/s11682-010-9088-x pmid: 20454594 |
| [8] |
Brewer JB, Magda S, Airriess C, et al. Fully-automated quantification of regional brain volumes for improved detection of focal atrophy in Alzheimer disease[J]. AJNR Am J Neuroradiol, 2009, 30(3):578-580.
doi: 10.3174/ajnr.A1402 URL |
| [9] |
Ahdidan J, Raji CA, DeYoe EA, et al. Quantitative Neuroimaging Software for Clinical Assessment of Hippocampal Volumes on MR Imaging[J]. J Alzheimers Dis, 2016, 49(3):723-732.
doi: 10.3233/JAD-150559 pmid: 26484924 |
| [10] |
Abrigo J, Shi L, Luo Y, et al. Standardization of hippocampus volumetry using automated brain structure volumetry tool for an initial Alzheimer′s disease imaging biomarker[J]. Acta Radiol, 2019, 60(6):769-776.
doi: 10.1177/0284185118795327 pmid: 30185071 |
| [11] |
Rathore S, Habes M, Iftikhar MA, et al. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer′s disease and its prodromal stages[J]. Neuroimage, 2017, 155:530-548.
doi: S1053-8119(17)30282-3 pmid: 28414186 |
| [12] |
Hill DLG, Schwarz AJ, Isaac M, et al. Coalition Against Major Diseases/European Medicines Agency biomarker qualification of hippocampal volume for enrichment of clinical trials in predementia stages of Alzheimer′s disease[J]. Alzheimers Dement, 2014, 10(4):421-429,e3.
doi: 10.1016/j.jalz.2013.07.003 URL |
| [13] |
Jack CR Jr, Knopman DS, Jagust WJ, et al. Hypothetical model of dynamic biomarkers of the Alzheimer′s pathological cascade[J]. Lancet Neurol, 2010, 9(1):119-128.
doi: 10.1016/S1474-4422(09)70299-6 URL |
| [14] |
Desikan RS, Cabral HJ, Hess CP, et al. Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer′s disease[J]. Brain, 2009, 132(Pt 8):2048-2057.
doi: 10.1093/brain/awp123 pmid: 19460794 |
| [15] |
Colliot O, Chételat G, Chupin M, et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus[J]. Radiology, 2008, 248(1):194-201.
doi: 10.1148/radiol.2481070876 URL |
| [16] |
Fleisher AS, Sun S, Taylor C, et al. Volumetric MRI vs clinical predictors of Alzheimer disease in mild cognitive impairment[J]. Neurology, 2008, 70(3):191-199.
doi: 10.1212/01.wnl.0000287091.57376.65 pmid: 18195264 |
| [17] |
McEvoy LK, Fennema-Notestine C, Roddey JC, et al. Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment[J]. Radiology, 2009, 251(1):195-205.
doi: 10.1148/radiol.2511080924 pmid: 19201945 |
| [18] |
Sarica A, Vasta R, Novellino F, et al. MRI Asymmetry Index of Hippocampal Subfields Increases Through the Continuum From the Mild Cognitive Impairment to the Alzheimer′s Disease[J]. Front Neurosci, 2018, 12:576.
doi: 10.3389/fnins.2018.00576 URL |
| [19] | Yue L, Wang T, Wang J, et al. Asymmetry of Hippocampus and Amygdala Defect in Subjective Cognitive Decline Among the Community Dwelling Chinese[J]. Front Psychiatry, 2018, 9:226. |
| [20] |
de Flores R, La Joie R, Chételat G. Structural imaging of hippocampal subfields in healthy aging and Alzheimer′s disease[J]. Neuroscience, 2015, 309:29-50.
doi: 10.1016/j.neuroscience.2015.08.033 pmid: 26306871 |
| [21] |
Dong M, Xie L, Das SR, et al. DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer′s disease[J]. Neuroimage, 2021, 243:118514.
doi: 10.1016/j.neuroimage.2021.118514 URL |
| [22] |
Sørensen L, Igel C, Liv Hansen N, et al. Early detection of Alzheimer′s disease using MRI hippocampal texture[J]. Hum Brain Mapp, 2016, 37(3):1148-1161.
doi: 10.1002/hbm.23091 pmid: 26686837 |
| [23] |
Zhao K, Ding Y, Han Y, et al. Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer′s disease diagnosis, longitudinal progress and biological basis[J]. Science Bulletin, 2020, 65(13):1103-1113.
doi: 10.1016/j.scib.2020.04.003 URL |
| [24] |
Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes[J]. Acta Neuropathol, 1991, 82(4):239-259.
doi: 10.1007/BF00308809 pmid: 1759558 |
| [25] |
de Toledo-Morrell L, Goncharova I, Dickerson B, et al. From healthy aging to early Alzheimer′s disease: in vivo detection of entorhinal cortex atrophy[J]. Ann N Y Acad Sci, 2000, 911:240-253.
doi: 10.1111/j.1749-6632.2000.tb06730.x URL |
| [26] |
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 URL |
| [27] |
Devanand DP, Pradhaban G, Liu X, et al. Hippocampal and entorhinal atrophy in mild cognitive impairment: prediction of Alzheimer disease[J]. Neurology, 2007, 68(11):828-836.
pmid: 17353470 |
| [28] |
deToledo-Morrell L, Stoub TR, Bulgakova M, et al. MRI-derived entorhinal volume is a good predictor of conversion from MCI to AD[J]. Neurobiol Aging, 2004, 25(9):1197-1203.
pmid: 15312965 |
| [29] |
Killiany RJ, Hyman BT, Gomez-Isla T, et al. MRI measures of entorhinal cortex vs hippocampus in preclinical AD[J]. Neurology, 2002, 58(8):1188-1196.
pmid: 11971085 |
| [30] |
Stoub TR, Rogalski EJ, Leurgans S, et al. Rate of entorhinal and hippocampal atrophy in incipient and mild AD: relation to memory function[J]. Neurobiol Aging, 2010, 31(7):1089-1098.
doi: 10.1016/j.neurobiolaging.2008.08.003 pmid: 18809228 |
| [31] |
Du AT, Schuff N, Kramer JH, et al. Higher atrophy rate of entorhinal cortex than hippocampus in AD[J]. Neurology, 2004, 62(3):422-427.
doi: 10.1212/01.WNL.0000106462.72282.90 URL |
| [32] |
Nikolenko VN, Oganesyan MV, Rizaeva NA, et al. Amygdala: Neuroanatomical and Morphophysiological Features in Terms of Neurological and Neurodegenerative Diseases[J]. Brain Sci, 2020, 10(8):502.
doi: 10.3390/brainsci10080502 URL |
| [33] |
Basso M, Yang J, Warren L, et al. Volumetry of amygdala and hippocampus and memory performance in Alzheimer′s disease[J]. Psychiatry Res, 2006, 146(3):251-261.
doi: 10.1016/j.pscychresns.2006.01.007 URL |
| [34] |
Basso M, Gelernter J, Yang J, et al. Apolipoprotein E epsilon4 is associated with atrophy of the amygdala in Alzheimer′s disease[J]. Neurobiol Aging, 2006, 27(10):1416-1424.
doi: 10.1016/j.neurobiolaging.2005.08.002 URL |
| [35] |
Poulin SP, Dautoff R, Morris JC, et al. Amygdala atrophy is prominent in early Alzheimer′s disease and relates to symptom severity[J]. Psychiatry Res, 2011, 194(1):7-13.
doi: 10.1016/j.pscychresns.2011.06.014 URL |
| [36] |
Barnes J, Whitwell JL, Frost C, et al. Measurements of the amygdala and hippocampus in pathologically confirmed Alzheimer disease and frontotemporal lobar degeneration[J]. Arch Neurol, 2006, 63(10):1434-1439.
doi: 10.1001/archneur.63.10.1434 URL |
| [37] |
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 pmid: 21339500 |
| [38] |
Miller MI, Younes L, Ratnanather JT, et al. Amygdalar atrophy in symptomatic Alzheimer′s disease based on diffeomorphometry: the BIOCARD cohort[J]. Neurobiol Aging, 2015, 36(Suppl 1):S3-S10.
doi: 10.1016/j.neurobiolaging.2014.06.032 URL |
| [39] |
Tang X, Holland D, Dale AM, et al. Baseline shape diffeomorphometry patterns of subcortical and ventricular structures in predicting conversion of mild cognitive impairment to Alzheimer′s disease[J]. J Alzheimers Dis, 2015, 44(2):599-611.
doi: 10.3233/JAD-141605 URL |
| [40] |
Tang X, Holland D, Dale AM, et al. The diffeomorphometry of regional shape change rates and its relevance to cognitive deterioration in mild cognitive impairment and Alzheimer′s disease[J]. Hum Brain Mapp, 2015, 36(6):2093-2117.
doi: 10.1002/hbm.22758 URL |
| [41] |
Chincarini A, Bosco P, Calvini P, et al. Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer′s disease[J]. Neuroimage, 2011, 58(2):469-480.
doi: 10.1016/j.neuroimage.2011.05.083 pmid: 21718788 |
| [42] | Zhao L, Luo Y, Lew D, et al. Risk estimation before progression to mild cognitive impairment and Alzheimer′s disease: an AD resemblance atrophy index[J]. Aging(Albany NY), 2019, 11(16):6217-6236. |
| [43] |
Mai Y, Yu Q, Zhu F, et al. AD Resemblance atrophy index as a diagnostic biomarker for Alzheimer′s disease: a retrospective clinical and biological validation[J]. J Alzheimers Dis, 2021, 79(3):1023-1032.
doi: 10.3233/JAD-201033 URL |
| [1] | . [J]. Journal of Diagnostics Concepts & Practice, 2022, 21(01): 18-21. |
| [2] | . [J]. Journal of Diagnostics Concepts & Practice, 2022, 21(01): 5-7. |
| [3] | . [J]. Journal of Diagnostics Concepts & Practice, 2022, 21(01): 8-11. |
| [4] | YANG Xiaochen, ZHU Yuan, SUN Qiong, YU Xiaoping, ZHANG Xian. Advance in study on influencing factors, evaluation and intervention of subjective cognitive decline [J]. Journal of Diagnostics Concepts & Practice, 2022, 21(01): 90-94. |
| [5] | WANG Tao, ZHANG Chencheng, SUN Bomin, LI Dianyou. Detection of electrode position in deep brain stimulation therapy in patients with Parkinson′s disease: A retrospective analysis [J]. Journal of Diagnostics Concepts & Practice, 2017, 16(02): 157-161. |
| Viewed | ||||||
|
Full text |
|
|||||
|
Abstract |
|
|||||
