[1] Sanford AM.  Mild Cognitive Impairment[J]. Clin Geriatr Med, 2017, 33(3): 325-337.   doi: 10.1016/j.cger.2017.02.005
[2] Roberts R, Knopman DS.  Classification and Epidemiology of MCI[J]. Clin Geriatr Med, 2013, 29(4): 753-772.   doi: 10.1016/j.cger.2013.07.003
[3] Michaud TL, Su D, Siahpush M, et al.  The Risk of Incident Mild Cognitive Impairment and Progression to Dementia Considering Mild Cognitive Impairment Subtypes[J]. Dement Geriatr Cogn Disord Extra, 2017, 7(1): 15-29.   doi: 10.1159/000452486
[4] Nie HW, Xu Y, Liu B, et al.  The prevalence of mild cognitive impairment about elderly population in China: a meta-analysis[J]. Int J Geriatr Psychiatry, 2011, 26(6): 558-563.   doi: 10.1002/gps.2579
[5] Mayo CD, Mazerolle EL, Ritchie L, et al.  Longitudinal changes in microstructural white matter metrics in Alzheimer's disease[J]. Neuroimage Clin, 2017, 13: 330-338.   doi: 10.1016/j.nicl.2016.12.012
[6] Yu HL, Chen ZJ, Zhao JW, et al.  Olfactory Impairment and Hippocampal Volume in a Chinese MCI Clinical Sample[J]. Alzheimer Dis Assoc Disord, 2019, 33(2): 124-128.   doi: 10.1097/WAD.0000000000000305
[7] Nickl-Jockschat T, Kleiman A, Schulz JB, et al.  Neuroanatomic changes and their association with cognitive decline in mild cognitive impairment: a meta-analysis[J]. Brain Struct Funct, 2012, 217(1): 115-125.   doi: 10.1007/s00429-011-0333-x
[8]

Kalin AM, Park MT, Chakravarty MM, et al. Subcortical Shape Changes, Hippocampal Atrophy and Cortical Thinning in Future Alzheimer's Disease Patients[J/OL]. Front Aging Neurosci, 2017, 9: 38 [2019-10-17]. https://www.frontiersin.org/articles/10.3389/fnagi.2017.00038/full. DOI: 10.3389/fnagi.2017.00038.

[9] Yushkevich PA, Pluta JB, Wang HZ, et al.  Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment[J]. Hum Brain Mapp, 2015, 36(1): 258-287.   doi: 10.1002/hbm.22627
[10] Evans TE, Adams HHH, Licher S, et al.  Subregional volumes of the hippocampus in relation to cognitive function and risk of dementia[J]. NeuroImage, 2018, 178: 129-135.   doi: 10.1016/j.neuroimage.2018.05.041
[11]

Broadhouse KM, Mowszowski L, Duffy S, et al. Memory Performance Correlates of Hippocampal Subfield Volume in Mild Cognitive Impairment Subtype[J/OL]. Front Behav Neurosci, 2019, 13: 259 [2019-10-17]. https://www.frontiersin.org/articles/10.3389/fnbeh.2019.00259/full. DOI: 10.3389/fnbeh.2019.00259.

[12] Razlighi QR, Oh H, Habeck C, et al.  Dynamic Patterns of Brain Structure-Behavior Correlation Across the Lifespan[J]. Cereb Cortex, 2017, 27(7): 3586-3599.   doi: 10.1093/cercor/bhw179
[13]

Bartel F, Vrenken H, Bijma F, et al. Regional analysis of volumes and reproducibilities of automatic and manual hippocampal segmentations[J/OL]. PLoS One, 2017, 12(2): e0166785[2019-10-17]. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0166785. DOI: 10.1371/journal.pone.0166785.

[14] Mulder ER, De Jong RA, Knol DL, et al.  Hippocampal volume change measurement: quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST[J]. Neuroimage, 2014, 92: 169-181.   doi: 10.1016/j.neuroimage.2014.01.058
[15] Chen J, Zhang ZJ, Li SJ.  Can multi-modal neuroimaging evidence from hippocampus provide biomarkers for the progression of amnestic mild cognitive impairment?[J]. Neurosci Bull, 2015, 31(1): 128-140.   doi: 10.1007/s12264-014-1490-8
[16] Carlesimo GA, Cherubini A, Caltagirone C, et al.  Hippocampal mean diffusivity and memory in healthy elderly individuals: a cross-sectional study[J]. Neurology, 2010, 74(3): 194-200.   doi: 10.1212/WNL.0b013e3181cb3e39
[17] Mak E, Gabel S, Su L, et al.  Multi-modal MRI investigation of volumetric and microstructural changes in the hippocampus and its subfields in mild cognitive impairment, Alzheimer's disease, and dementia with Lewy bodies[J]. Int Psychogeriatr, 2017, 29(4): 545-555.   doi: 10.1017/S1041610216002143
[18] Müller MJ, Greverus D, Weibrich C, et al.  Diagnostic utility of hippocampal size and mean diffusivity in amnestic MCI[J]. Neurobiol Aging, 2007, 28(3): 398-403.   doi: 10.1016/j.neurobiolaging.2006.01.009
[19] Den Heijer T, Van Der Lijn F, Vernooij MW, et al.  Structural and diffusion MRI measures of the hippocampus and memory performance[J]. Neuroimage, 2012, 63(4): 1782-1789.   doi: 10.1016/j.neuroimage.2012.08.067
[20] Buchbinder BR.  Functional magnetic resonance imaging[J]. Handbook of Clinical Neurology, 2016, 135: 61-92.   doi: 10.1016/B978-0-444-53485-9.00004-0
[21]

De Marco M, Ourselin S, Venneri A. Age and hippocampal volume predict distinct parts of default mode network activity [J/OL]. Sci Rep, 2019, 9(1): 16075 [2019-10-17]. https://www.nature.com/articles/s41598-019-52488-9. DOI: 10.1038/s41598−019−52488−9.

[22]

Lin L, Xing GQ, Han Y. Advances in Resting State Neuroimaging of Mild Cognitive Impairment[J/OL]. Front Psychiatry, 2018, 9: 671[2019-10-17]. https://www.frontiersin.org/articles/10.3389/fpsyt.2018.00671/full. DOI: 10.3389/fpsyt.2018.00671.

[23] Cai SP, Chong T, Peng YL, et al.  Altered functional brain networks in amnestic mild cognitive impairment: a resting-state fMRI study[J]. Brain Imaging Behav, 2017, 11(3): 619-631.   doi: 10.1007/s11682-016-9539-0
[24] Bai F, Zhang Z, Watson DR, et al.  Abnormal functional connectivity of hippocampus during episodic memory retrieval processing network in amnestic mild cognitive impairment[J]. Biol Psychiatry, 2009, 65(11): 951-958.   doi: 10.1016/j.biopsych.2008.10.017
[25]

Bai F, Xie CM, Watson DR, et al. Aberrant Hippocampal Subregion Networks Associated with the Classifications of aMCI Subjects: A Longitudinal Resting-State Study[J/OL]. PLoS One, 2011, 6(12): e29288 [2019-10-17]. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0029288. DOI: 10.1371/journal.pone.0029288.

[26] Xie CM, Li WJ, Chen G, et al.  Late-life depression, mild cognitive impairment and hippocampal functional network architecture[J]. Neuroimage Clin, 2013, 3: 311-320.   doi: 10.1016/j.nicl.2013.09.002
[27] Wang ZQ, Liang PP, Jia XQ, et al.  Baseline and longitudinal patterns of hippocampal connectivity in mild cognitive impairment: evidence from resting state fMRI[J]. J Neurol Sci, 2011, 309(1/2): 79-85.   doi: 10.1016/j.jns.2011.07.017
[28] Rice L, Bisdas S.  The diagnostic value of FDG and amyloid PET in Alzheimer's disease-A systematic review[J]. Eur J Radiol, 2017, 94: 16-24.   doi: 10.1016/j.ejrad.2017.07.014
[29] 段小艺, 刘翔, 叶佳俊, 等.  阿尔茨海默病及轻度认知损伤患者PET与MRI分析[J]. 中国医学影像技术, 2017, 33(11): 1624-1629.   doi: 10.13929/j.1003-3289.201708031
Duan XY, Liu X, Ye JJ, et al.  Analysis of PET and MRI in Alzheimer disease and mild cognitive impairment[J]. Chin J Med Imaging Technol, 2017, 33(11): 1624-1629.   doi: 10.13929/j.1003-3289.201708031
[30]

Ferrari BL, Neto GCC, Nucci MP, et al. The accuracy of hippocampal volumetry and glucose metabolism for the diagnosis of patients with suspected Alzheimer's disease, using automatic quantitative clinical tools[J/OL]. Medicine (Baltimore), 2019, 98(45): e17824[2019-10-17]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6855664. DOI: 10.1097/MD.0000000000017824.

[31] De Santi S, De Leon MJ, Rusinek H, et al.  Hippocampal formation glucose metabolism and volume losses in MCI and AD[J]. Neurobiol Aging, 2001, 22(4): 529-539.   doi: 10.1016/s0197-4580(01)00230-5
[32] Li Y, Rinne JO, Mosconi L, et al.  Regional analysis of FDG and PIB-PET images in normal aging, mild cognitive impairment, and Alzheimer's disease[J]. Eur J Nucl Med Mol Imaging, 2008, 35(12): 2169-2181.   doi: 10.1007/s00259-008-0833-y
[33] Ouchi Y, Nobezawa S, Okada H, et al.  Altered glucose metabolism in the hippocampal head in memory impairment[J]. Neurology, 1998, 51(1): 136-142.   doi: 10.1212/wnl.51.1.136
[34] Choi EJ, Son YD, Noh Y, et al.  Glucose Hypometabolism in Hippocampal Subdivisions in Alzheimer's Disease: A Pilot Study Using High-Resolution 18F-FDG PET and 7.0-T MRI[J]. J Clin Neurol, 2018, 14(2): 158-164.   doi: 10.3988/jcn.2018.14.2.158
[35] Lane CA, Hardy J, Schott JM.  Alzheimer's disease[J]. Eur J Neurol, 2018, 25(1): 59-70.   doi: 10.1111/ene.13439
[36] Koivunen J, Scheinin N, Virta JR, et al.  Amyloid PET imaging in patients with mild cognitive impairment: a 2-year follow-up study[J]. Neurology, 2011, 76(12): 1085-1090.   doi: 10.1212/WNL.0b013e318212015e
[37]

Ong K, Villemagne VL, Bahar-Fuchs A, et al. 18F-florbetaben Aβ imaging in mild cognitive impairment[J/OL]. Alzheimers Res Ther, 2013, 5(1): 4 [2019-10-17]. https://alzres.biomedcentral.com/articles/10.1186/alzrt158. DOI: 10.1186/alzrt158.

[38] Barthel H, Gertz HJ, Dresel S, et al.  Cerebral amyloid-β PET with florbetaben (18F) in patients with Alzheimer's disease and healthy controls: a multicentre phase 2 diagnostic study[J]. Lancet Neurol, 2011, 10(5): 424-435.   doi: 10.1016/S1474-4422(11)70077-1
[39] Ossenkoppele R, Jansen WJ, Rabinovici GD, et al.  Prevalence of Amyloid PET Positivity in Dementia Syndromes: A Meta-analysis[J]. JAMA, 2015, 313(19): 1939-1949.   doi: 10.1001/jama.2015.4669
[40] Jansen WJ, Ossenkoppele R, Knol DL, et al.  Prevalence of Cerebral Amyloid Pathology in Persons without Dementia: A Meta-analysis[J]. JAMA, 2015, 313(19): 1924-1938.   doi: 10.1001/jama.2015.4668
[41]

Cerami C, Dodich A, Iannaccone S, et al. A biomarker study in long-lasting amnestic mild cognitive impairment[J/OL]. Alzheimers Res Ther, 2018, 10(1): 42 [2019-10-17]. https://alzres.biomedcentral.com/articles/10.1186/s13195-018-0369-8. DOI: 10.1186/s13195−018−0369−8.

[42] Marquié M, Normandin MD, Vanderburg CR, et al.  Validating novel tau positron emission tomography tracer [F-18]-AV-1451(T807) on postmortem brain tissue[J]. Ann Neurol, 2015, 78(5): 787-800.   doi: 10.1002/ana.24517
[43] Ossenkoppele R, Rabinovici GD, Smith R, et al.  Discriminative Accuracy of [18F] flortaucipir Positron Emission Tomography for Alzheimer Disease vs Other Neurodegenerative Disorders[J]. JAMA, 2018, 320(11): 1151-1162.   doi: 10.1001/jama.2018.12917
[44] Ossenkoppele R, Schonhaut DR, Schöll M, et al.  Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer's disease[J]. Brain, 2016, 139(5): 1551-1567.   doi: 10.1093/brain/aww027
[45] Göttler J, Preibisch C, Riederer I, et al.  Reduced blood oxygenation level dependent connectivity is related to hypoperfusion in Alzheimer's disease[J]. J Cereb Blood Flow Metab, 2019, 39(7): 1314-1325.   doi: 10.1177/0271678X18759182
[46] Riederer I, Bohn KP, Preibisch C, et al.  Alzheimer Disease and Mild Cognitive Impairment: Integrated Pulsed Arterial Spin-Labeling MRI and 18F-FDG PET[J]. Radiology, 2018, 288(1): 198-206.   doi: 10.1148/radiol.2018170575
[47] Zhao ZL, Fan FM, Lu J, et al.  Changes of gray matter volume and amplitude of low-frequency oscillations in amnestic MCI: An integrative multi-modal MRI study[J]. Acta Radiol, 2015, 56(5): 614-621.   doi: 10.1177/0284185114533329
[48] Cho ZH, Son YD, Kim HK, et al.  Substructural Hippocampal Glucose Metabolism Observed on PET/MRI[J]. J Nucl Med, 2010, 51(10): 1545-1548.   doi: 10.2967/jnumed.110.076182
[49] Kwak K, Yun HJ, Park G, et al.  Multi-Modality Sparse Representation for Alzheimer's Disease Classification[J]. J Alzheimers Dis, 2018, 65(3): 807-817.   doi: 10.3233/JAD-170338
[50] Tahmasian M, Pasquini L, Scherr M, et al.  The lower hippocampus global connectivity, the higher its local metabolism in Alzheimer disease[J]. Neurology, 2015, 84(19): 1956-1963.   doi: 10.1212/WNL.0000000000001575
[51] Jhoo JH, Lee DY, Choo IH, et al.  Discrimination of normal aging, MCI and AD with multimodal imaging measures on the medial temporal lobe[J]. Psychiatry Res: Neuroimaging, 2010, 183(3): 237-243.   doi: 10.1016/j.pscychresns.2010.03.006
[52] Miller-Thomas MM, Benzinger TLS.  Neurologic Applications of PET/MR Imaging[J]. Magn Reson Imaging Clin N Am, 2017, 25(2): 297-313.   doi: 10.1016/j.mric.2016.12.003
[53] Henriksen OM, Marner L, Law I.  Clinical PET/MR Imaging in Dementia and Neuro-Oncology[J]. PET Clin, 2016, 11(4): 441-452.   doi: 10.1016/j.cpet.2016.05.003
[54] Marchitelli R, Aiello M, Cachia A, et al.  Simultaneous resting-state FDG-PET/fMRI in Alzheimer Disease: Relationship between glucose metabolism and intrinsic activity[J]. NeuroImage, 2018, 176: 246-258.   doi: 10.1016/j.neuroimage.2018.04.048
[55] Scherr M, Utz L, Tahmasian M, et al.  Effective connectivity in the default mode network is distinctively disrupted in Alzheimer's disease-A simultaneous resting-state FDG-PET/fMRI study[J]. Hum Brain Mapp, 2019., : -.   doi: 10.1002/hbm.24517