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Chinese Journal of Alzheimer's Disease and Related Disorders

Abbreviation (ISO4): Chinese Journal of Alzheimer's Disease and Related Disorders      Editor in chief: Jun WANG

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Neuropsychological and neuroimaging characteristics of early-onset and late-onset Alzheimer’s disease

  • LI Zheyu 1 ,
  • LI Kaicheng 2 ,
  • ZENG Qingze 2 ,
  • CHEN Yanxing , 1 ,
  • ZHANG Minming , 2
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  • 1 Department of Neurology, 2 Affiliated Hospital of Zhejiang University School of Medicine
  • 2 Department of Radiology, 2 Affiliated Hospital of Zhejiang University School of Medicine

Received date: 2019-07-10

  Revised date: 2019-07-17

  Online published: 2019-12-25

Abstract

Alzheimer’s disease (AD) can be classified into early-onset Alzheimer’s disease (EOAD) and late-onset Alzheimer’s disease (LOAD), depending on the age of disease onset. Thereinto, EOAD shows a rapid progression after onset and thus requires earlier diagnosis. Identification of differences in clinical manifestations, including the imaging features, would be important in early detection of the two subtypes. Although EOAD patients can be confirmed with special symptoms, including visuo-spatial dysfunctions, there still remains other overlaps between EOAD and LOAD which requires an early and accurate diagnosis. This review provides an overview of two subtypes and tries to provide some references for diagnosis, especially for EOAD.

Cite this article

LI Zheyu , LI Kaicheng , ZENG Qingze , CHEN Yanxing , ZHANG Minming . Neuropsychological and neuroimaging characteristics of early-onset and late-onset Alzheimer’s disease[J]. Chinese Journal of Alzheimer's Disease and Related Disorders, 2019 , 2(4) : 537 -543 . DOI: 10.3969/j.issn.2096-5516.2019.04.014

阿尔茨海默病(Alzheimer’s disease, AD)是老年期痴呆最常见的类型,根据起病年龄是否小于65岁[1],可进一步分为早发型(early-onset AD, EOAD)和晚发型AD(late-onset AD, LOAD)[2]。尽管在老年人群中,EOAD所占的比例不足5%[3-4],但其临床进展迅速,预后差,因此早期诊断尤为重要。EOAD和LOAD临床表现有所不同,LOAD患者主要表现为近事记忆下降,而EOAD患者则更多地出现视空间、语言、注意和执行功能受损。既往磁共振成像(MRI)和正电子发射计算机断层显像(PET)研究表明,与LOAD相比,EOAD患者的脑萎缩范围更大,涉及额顶叶和楔前叶区域,且常伴有背外侧前额叶网络功能连接及扣带回与枕叶代谢连接的受损。不同位点的基因突变是AD重要的致病因素[5]。近年来,基因相关研究识别出许多新的风险基因位点,进一步揭示AD的发病机制,为治疗提供新的方向[6]。此外,MRI、PET等先进技术的应用有助于AD的早期诊断[7]。本文对EOAD和LOAD的临床特征、遗传因素及神经影像学研究进行综述,并指出进一步的研究方向,以期为该病的诊断与治疗提供一定参考。

1 临床特征

与LOAD患者相比,EOAD患者的基线总体认知水平较低,简易智力状态检查(mini-mental state examination, MMSE)得分更低,且临床进展快。除记忆障碍外,EOAD患者常出现视空间、语言、注意力和执行等认知功能障碍[8-12]。一项纳入EOAD与LOAD患者各435例的研究发现EOAD患者的执行功能、视空间功能和视觉记忆等认知功能较LOAD差,且常伴有情感淡漠[13]。需要注意的是,EOAD常出现较不明显的情景记忆障碍,需避免错误诊断。此外,根据Joshi等[14]的研究报告,约20%的EOAD患者有家族史,且常有一些较为特殊的临床表现,如步态异常、癫痫发作和肌阵挛。这些表现可能与小脑淀粉样沉积等异常病理改变有关[15]。尽管上述症状罕见,但仍需关注。
既往纵向研究报道[11,16 -17],EOAD在非记忆功能方面的损害进展迅速,尤其是在女性及高等教育群体中。然而也有一些学者认为起病年龄并不一定是预测进展速度的指标[18],上述结论的差异可能是由随访期较短、心理评估测试过于简易等因素造成。相比之下,LOAD常以记忆障碍起病,特别是情景记忆损害[19],并随着疾病进展逐渐出现其他认知域的损害。

2 遗传因素

APOE ε4等位基因是AD的重要风险基因,在EOAD和LOAD中发生比例相似[20]。APOE ε4等位基因通过影响体内胆固醇转运蛋白和其他载脂蛋白的生成进一步影响AD发生发展[21]。研究表明,携带APOE ε4等位基因的AD患者较非携带者的海马萎缩更为严重,提示更为严重的记忆损害[22-23]。一项基于荷兰人群数据库的研究表明,携带APOE ε4等位基因会显著提高有AD家族史的个体罹患EOAD的风险[24-25]。此外Davidson等[26]的研究表明,APOE ε4等位基因在发病年龄位于60至70岁之间的AD人群中影响较大,即LOAD中APOE ɛ4等位基因携带者的起病年龄比非携带者更早,而EOAD中APOE ε4等位基因携带者的起病年龄较非携带者晚。
基因突变对EOAD的发生发展有着显著的影响[27]。研究表明,5%的EOAD会发生常染色体显性遗传的早老素1(PSEN1)、早老素2(PSEN2)和淀粉样前体蛋白(APP)突变[28],这些患者常被归类于家族性AD[29]。一项大样本基因研究表明,EOAD中PSEN1、PSEN2和APP基因突变率分别约为6%、1%及< 1%[30]。这3种基因突变是通过增加淀粉样蛋白Aβ42(来自于淀粉样前体蛋白)的堆积而致病。具体来说,PSEN1可能与一些AD非典型临床症状有关,如语言损害、错觉、幻觉、情感淡漠和痉挛性截瘫等[31]。此外,Rovelet-Lecrux等[32]提出APP位点复制会导致Aβ在脑实质和血管沉积,从而导致EOAD伴发脑淀粉样血管病。PSEN2突变和PSEN1效应相似,但相对少见。此外,有报道指出TREM2 R47H、TYROBP等基因变异同样能增加EOAD的患病风险[33-34]
LOAD相关的风险基因比EOAD更为复杂,其中包括基因间相互作用、基因与环境间的交互作用等。除了主要的风险基因APOE外,其他基因变异如ABCA7、CASS4、CD33、CD2AP、CR1、BIN1、INPP5D、APP、TREM2及PLD3等亦见报道[35-38]。这些基因变异的致病作用主要涉及3种途径:炎症反应,脂质代谢稳态和细胞内吞作用。因篇幅有限,不再详述。

3 神经影像研究进展

AD的病理改变主要包括细胞外β-淀粉样蛋白(Aβ)沉积和细胞内神经原纤维缠结,通常认为上述病理过程在出现临床症状的10年前就已存在。淀粉样蛋白斑块和神经原纤维缠结干扰了钙信号传导和突触传递,造成突触和神经元的丢失,最终引起大脑结构及功能的改变。以往组织病理和临床研究表明EOAD和LOAD的Aβ沉积和脑代谢情况存在差异,影像学研究也发现脑萎缩、功能网络改变的模式不同。多模态神经影像研究是反映AD病理机制的重要手段。近年,基于体素的形态测量学(voxel-based morphometry, VBM)和弥散张量成像(diffusion tensor imaging, DTI)已广泛应用于分析灰质和白质的改变。同时,借助对脑神经网络的认识,静息态磁共振成像(resting-state functional MRI, rs-fMRI)被应用于AD病理改变的研究中,主要关注于默认模式网络(default mode network, DMN,功能上涉及到自传信息、情绪、自我参照和情景性记忆等领域)、前内侧颞叶网络(anterior-medial temporal network, ATN,功能上涉及陈述性记忆的某些方面)、背外侧前额叶网络(dorso-lateral prefrontal network, DLPFN,与执行能力相关)和“富人俱乐部”(rich club,被认为是大脑的核心区域,有着更密集的连接,涉及到大脑各个功能区信息的整合)等方面。

3.1 结构磁共振成像

EOAD较LOAD的灰质萎缩范围更为广泛,且模式不同。研究表明,EOAD的灰质萎缩累及大部分新皮质区,但初级感觉皮质、运动皮质、视觉皮质、扣带回前部和额眶部皮质结构尚未受累[39]。Frisoni等[39]使用VBM发现EOAD的额顶叶萎缩更明显,而LOAD的内侧颞叶萎缩更为显著。该结果与2种AD亚型的临床特点基本相符,即EOAD常较早出现执行功能损害,而LOAD常以记忆损害为早期特征。此外,有证据表明楔前叶是EOAD最易发生萎缩和代谢减低的区域[9],并且这些患者往往伴有视空间功能的改变。EOAD患者与LOAD患者的痴呆评定量表(CDR)得分相似,但大脑萎缩更严重。根据“认知储备(cognitive reservation)”假说,可能由于EOAD患者有更多的脑储备,从而对脑结构和功能的损伤拥有更强的抵抗力[40-41],即出现更广泛的萎缩时才表现出同样的损害。另一方面,有研究表明EOAD的脑萎缩和认知功能损害进展较LOAD迅速。Cho等[16]证实EOAD在语言、注意、执行功能方面有显著减退,同时相关皮质的萎缩快速进展。然而,Migliaccio等[42]用基于张量的形态测量学分析却发现EOAD患者1年内大脑萎缩程度(最大值5.9%)低于LOAD(最大值14.5%)。这些不一致的结果可能是随访时间与分析方法的差异造成的。
关于白质束完整性的研究,也存在矛盾的结果。一方面,目前认为EOAD常出现广泛的白质脱髓鞘损伤,尤其是在胼胝体压部和背侧颞顶叶区域,而在LOAD中经常观察到海马旁回白质束的选择性损伤[43]。另一方面,Daianu等[44]结合自动多图谱白质束提取并用同样的弥散测量方法确认受损的通路,发现EOAD中白质的改变更多涉及到后部白质——主要是胼胝体的顶叶部分和双侧扣带回腹侧部。值得注意的是,该研究中EOAD患者平均年龄较小,可能对结果造成影响。此外,Acosta-Cabronero等[45]工作证明,EOAD可能首先出现海马旁回与扣带回的损害,然后逐渐累及邻近颞顶叶区域。

3.2 脑功能网络

大脑是由神经元和无数有序交通的连接构成的神经网络。既往研究主要关注EOAD和LOAD各自的神经网络改变,较少研究对这两种亚型的神经网络改变进行比较。
一项纳入EOAD与LOAD各14例的研究中,Gour等[2]采用基于种子点的功能连接(后扣带皮质,双侧内嗅皮层与背外侧前额叶)来探究EOAD脑功能改变。该研究结果表明,两种亚型在ATN和DLPFN中的功能连接呈现出不同的变化模式。和对照组相比,EOAD在DLPFN的功能连接往往降低,而在ATN有所增加,与其在视觉识别记忆任务中的较好表现相符。相比之下,LOAD在ATN功能连接降低,而在DLPFN中功能连接增加,这与其较好的执行功能吻合。总之,这些结果是代偿机制的体现,也就是既往研究提出的过度激活[46-47]。与此相似,Adriaanse等[8]讨论了8个静息态神经网络的功能连接,包括内侧视觉系统、外侧视觉系统、听觉系统、感觉运动系统、双侧背侧视觉系统、执行控制网络和默认模式网络,结果发现EOAD在听觉、感觉运动、背侧视觉区的功能连接相对于LOAD显著降低。
上述两项研究的结果大体相似,但仍存在一些差异,目前导致这些差异的原因尚不清楚。在Gour等[2]的研究中,和对照组相比,EOAD和LOAD的DMN功能连接受损情况相似。然而Adriaanse等[8]报道EOAD患者DMN功能连接损害在顶叶更严重。上述结果的差异可能是由于使用不同的测定方法造成的:前者使用了基于种子点的连接分析,而后者应用了双重回归对网络及其联系进行整合分析。
近来,有研究应用网络分析展示出脑神经网络紊乱。如Daianu等[48]使用弥散加权成像、纤维追踪技术和皮层分割技术发现EOAD患者在双侧中央前回、楔前叶、后扣带回以及左侧舌回存在明显损伤。

3.3 脑代谢与灌注成像

研究表明EOAD和LOAD的脑代谢变化分布特点不同。通过比较EOAD和LOAD的氟-18-脱氧葡萄糖(18F-FDG) PET, Kim等[49]发现EOAD患者顶叶、额叶和皮质下区域(基底节和丘脑)代谢减低更为严重。有研究[50]发现EOAD患者18F-FDG摄取率下降最显著的区域位于顶叶。与之相比,LOAD通常是内侧颞叶摄取率下降最显著[51-52]
进一步研究分析显示,EOAD和LOAD存在脑内代谢连接的差异。Chung等[53]首次应用FDG-PET来观察EOAD和LOAD代谢连接的差异。与对照组相比,EOAD的代谢连接下降主要集中在扣带回和枕叶区域,而LOAD主要在枕叶和颞叶区域。研究表明EOAD患者有显著的视空间障碍,且静息态功能网络的下降更明显[2,8]。同时,参照疾病严重程度和CDR得分,EOAD糖代谢连接方面的恶化速度更为迅速,尤其是扣带回和枕叶区域[54]。而LOAD同质改变和低敏感性假说能够用来解释其未出现代谢连接下降的原因。值得注意的是,EOAD扣带回背侧和LOAD的运动辅助区均出现连接性增加,这可能是代偿激活或是其他部位连接性显著下降造成的相对增强。
结合临床表现能够更好理解这些异常改变。Ballarini等[54]纳入27例EOAD患者,并利用体素水平相关分析发现,患者行为异常与大脑代谢活动特定的功能紊乱有关。目前认为部分脑区过度激活与额叶、边缘系统的糖代谢增加有关,而情感淡漠与双侧额眶部、背外侧额叶皮质的改变相关。
脑灌注改变是AD相关的特征性改变之一。基于动脉自旋标记法(ASL)成像,有学者报道EOAD患者颞顶叶的灌注显著减少[55]。一项应用单光子发射计算机断层成像(single photon emission computed tomography, SPECT)的研究也报道,EOAD颞顶叶灌注减低,而LOAD内侧颞叶灌注显著下降[52]。总的来说,联合18F-FDG PET和ASL作为对结构磁共振的补充,有助于鉴别诊断EOAD,但仍需后续研究的证实。

3.4 7.0T脑铁成像

过去的研究发现,某些金属(如铜、锌和铁)能够在Aβ沉积过程中促进Aβ寡聚体形成。van Rooden等[56]研究发现,7.0T磁共振T2*加权序列图像中的相位信息能反映铁沉积的情况,并有可能进一步揭示AD病理过程和更为特异的Aβ沉积。基于这个结论,van Rooden等[57]通过计算峰相位移和区域平均相位差异来比较皮质中局部铁相关的差异,发现EOAD患者所有脑叶的峰相位移均有增加,其中额中回、内侧额叶上部、前中部扣带回、中央后回、顶上回、顶下回和楔前叶平均相位差异较LOAD高。基于铁沉积与Aβ沉积的相关性,可以推论EOAD特定区域的相位差异可能反映局部Aβ沉积。因此,7.0T脑铁成像提供了一种探索EOAD和LOAD发病机制差异的新途径。

3.5 淀粉样蛋白PET

尸检研究证实EOAD具有更多的β淀粉样斑块和神经原纤维缠结[58-59]。PET能够在体检测淀粉样蛋白沉积和神经原纤维缠结,是目前无创检测AD相关神经病理的最佳手段。既往较少PET研究探索EOAD和LOAD Aβ沉积模式差异,且亚型的年龄分界不明确。Ossenkoppele等[60]将100例AD患者根据中位年龄(62岁)分成年轻组和年老组,基于Pittsburgh compound B (PiB)-PET比较淀粉样蛋白沉积情况。分析结果显示,EOAD患者顶叶Aβ沉积增加,可能与视空间功能下降有关。Cho等[61]发现,EOAD与LOAD的全脑平均PiB摄取率无显著差异,而相比于LOAD患者(起病年龄≥70岁),EOAD患者(起病年龄< 60岁)在双侧丘脑、双侧基底节、左侧颞叶上部皮质和左侧楔叶的PiB值更高,这也在一定程度上阐释了EOAD中更常出现锥体外系症状和额叶损伤表现的原因。值得注意的是,考虑到年龄相关的大脑萎缩可能对结果造成影响,确立统一的分类标准格外重要。
与此相反,一项应用65岁作为分组标准的研究中,Rabinovici等[62]提出两种亚型全脑及局部的PiB摄取率无明显差异。此外,有研究表明淀粉样蛋白沉积与AD临床和解剖的异质性不相关[63]。可能Aβ的沉积并不是AD的核心病理过程,这需要进一步研究探索。

4 结语

为了更清楚的认识EOAD和LOAD,应采用更为先进的成像技术全面了解EOAD和LOAD。第一,未来的研究应关注于EOAD的纵向研究,探究其认知域变化的规律;第二,大多数关于EOAD和LOAD的研究是使用VBM来分析结构像,而Freesurfer能更为有效地匹配同源皮质区域并分别展示皮质厚度与表面区域,其应用将有助于发现多方面脑结构的差异;第三,应当更加关注功能连接、脑代谢网络以及富节点区域,这些对开展神经网络调节治疗(例如长距离功能连接:半球间功能连接)的研究有重要意义。最后,7.0T脑铁成像目前是一个运用较少的技术,其在AD领域的研究有较大潜在价值,值得密切关注。此外,也应厘清EOAD和LOAD的病理过程是否存在某些差异,这有助于尽早攻克AD。
[1]
Amaducci, LA, Rocca, WA, Schoenberg, BS, et al. Origin of the distinction between Alzheimer’s disease and senile dementia: how history can clarify nosology[J]. Neurology, 1986, 36(11):1497-1499.

PMID

[2]
Gour, N, Felician, O, Didic, M, et al. Functional connectivity changes differ in early and late-onset Alzheimer’s disease[J]. Hum Brain Mapp, 2014, 35(7): 2978-2994.

DOI

[3]
Bertram, L, Tanzi, RE, The genetics of Alzheimer’s disease[J]. Prog Mol Biol Transl Sci, 2012, 107:79-100.

[4]
Bruni, AC, Conidi, ME, Bernardi, L, et al. Genetics in degenerative dementia: current status and applicability[J]. Alzheimer Dis Assoc Disord, 2014, 28(3):199-205.

DOI

[5]
Lane, CA, Hardy, J, Schott, JM,et al Alzheimer’s disease[J]. Eur J Neurol, 2018, 25(1):59-70.

DOI PMID

[6]
Cuyvers, E Sleegers, K, Genetic variations underlying Alzheimer’s disease: evidence from genome-wide association studies and beyond[J]. Lancet Neurol, 2016, 15(8):857-868.

DOI PMID

[7]
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

[8]
Adriaanse, SM, Binnewijzend, MA, Ossenkoppele, R, et al. Widespread disruption of functional brain organization in early-onset Alzheimer’s disease[J]. PLoS One, 2014, 9(7):e102995.

DOI

[9]
Karas, G, Scheltens, P, Rombouts, S, et al. Precuneus atrophy in early-onset Alzheimer’s disease: a morphometric structural MRI study[J]. Neuroradiol, 2007, 49(12):967-976.

DOI

[10]
Koedam, EL, Lauffer, V, van der Vlies, AE, et al. Early- versus late-onset Alzheimer’s disease: more than age alone[J]. J Alzheimers Dis, 2010, 19(4):1401-1408.

DOI

[11]
Koss, E, Edland, S, Fillenbaum, G, et al. Clinical and neuropsychological differences between patients with earlier and later onset of Alzheimer’s disease: a CERAD analysis, Part XII[J]. Neurol, 1996, 46(1):136-141.

[12]
Stopford, CL, Snowden, JS, Thompson, JC, et al. Variability in cognitive presentation of Alzheimer’s disease[J]. Cortex, 2008, 44(2):185-195.

DOI PMID

[13]
Park, HK, Choi, SH, Park, SA, et al. Cognitive profiles and neuropsychiatric symptoms in Korean early-onset Alzheimer’s disease patients: a CREDOS study[J]. J Alzheimers Dis, 2015, 44(2):661-673.

DOI

[14]
Joshi, A, Ringman, JM, Lee, AS, et al. Comparison of clinical characteristics between familial and non-famIlIal early onset Alzheimer’s disease[J]. J Neurol, 2012, 259(10):2182-2188.

DOI

[15]
Houlden, H, Baker, M, McGowan, E, et al. Variant Alzheimer’s disease with spastic paraparesis and cotton wool plaques is caused by PS-1 mutations that lead to exceptionally high amyloid-beta concentrations[J]. Ann Neurol, 2000, 48(5):806-808.

PMID

[16]
Cho, H, Jeon, S, Kang, SJ, et al. Longitudinal changes of cortical thickness in early-versus late-onset Alzheimer’s disease[J]. Neurobiol Aging, 2013, 34(7):1921. e1929-1921. e1915.

[17]
Yoon, B, Shim, YS, Park, HK, et al. Predictive factors for disease progression in patients with early-onset Alzheimer’s disease[J]. J Alzheimers Dis, 2016, 49(1): 85-91.

DOI

[18]
Ortof, E, Crystal, HA, Rate of progression of Alzheimer’s disease[J]. J Am Geriatr Soc, 1989, 37(6):511-514.

PMID

[19]
Joubert, S, Gour, N, Guedj, E, et al. Early-onset and late- onset Alzheimer’s disease are associated with distinct patterns of memory impairment[J]. Cortex, 2016, 74: 217-232.

DOI PMID

[20]
Panegyres, PK, Chen, HY, Differences between early and late onset Alzheimer’s disease[J]. Am J Neurodegener Dis, 2013, 2(4):300-306.

[21]
Heinzen, EL, Need, AC, Hayden, KM, et al. Genome- wide scan of copy number variation in late-onset Alzheimer’s disease[J]. J Alzheimers Dis, 2010, 19(1): 69-77.

DOI PMID

[22]
Lind, J, Larsson, A, Persson, J, et al. Reduced hippocampal volume in non-demented carriers of the apolipoprotein E epsilon4: relation to chronological age and recognition memory[J]. Neurosci Lett, 2006, 396(1): 23-27.

DOI

[23]
Crivello, F, Lemaitre, H, Dufouil, C, et al. Effects of ApoE-epsilon4 allele load and age on the rates of grey matter and hippocampal volumes loss in a longitudinal cohort of 1186 healthy elderly persons[J]. Neuroimage, 2010, 53(3):1064-1069.

DOI PMID

[24]
Wijsman, EM, Daw, EW, Yu, X, et al. APOE and other loci affect age-at-onset in Alzheimer’s disease families with PS2 mutation[J]. Am J Med Genet B Neuropsychiatr Genet, 2005, 132B(1):14-20.

DOI

[25]
van Duijn, CM, de Knijff, P, Cruts, M, et al. Apolipoprotein E4 allele in a population-based study of early-onset Alzheimer’s disease[J]. Nat Genet, 1994, 7(1):74-78.

PMID

[26]
Davidson, Y, Gibbons, L, Pritchard, A, et al. Apolipoprotein E epsilon4 allele frequency and age at onset of Alzheimer’s disease[J]. Dement Geriatr Cogn Disord, 2007, 23(1):60-66.

DOI

[27]
Wingo, TS, Lah, JJ, Levey, AI, et al. Autosomal recessive causes likely in early-onset Alzheimer disease[J]. Arch Neurol, 2012, 69(1):59-64.

DOI

[28]
Mendez, MF, Early-onset Alzheimer’s disease: nonamnestic subtypes and type 2 AD[J]. Arch Med Res, 2012, 43(8):677-685.

DOI PMID

[29]
Barber, IS, Garcia-Cardenas, JM, Sakdapanichkul, C, et al. Screening exons 16 and 17 of the amyloid precursor protein gene in sporadic early-onset Alzheimer’s disease[J]. Neurobiol Aging, 2016, 39:220e221-227.

[30]
Cruts, M, van Duijn, CM, Backhovens, H, et al. Estimation of the genetic contribution of presenilin-1 and-2 mutations in a population-based study of presenile Alzheimer disease[J]. Hum Mol Genet, 1998, 7(1):43-51.

PMID

[31]
Ryan, NS, Rossor, MN, Correlating familial Alzheimer’s disease gene mutations with clinical phenotype[J]. Biomark Med, 2010, 4(1):99-112.

DOI

[32]
Rovelet-Lecrux, A, Hannequin, D, Raux, G, et al. APP locus duplication causes autosomal dominant early-onset Alzheimer disease with cerebral amyloid angiopathy[J]. Nat Genet, 2006, 38(1):24-26.

PMID

[33]
Pottier, C, Wallon, D, Rousseau, S, et al. TREM2 R47H variant as a risk factor for early-onset Alzheimer’s disease[J]. J Alzheimers Dis, 2013, 35(1):45-49.

DOI PMID

[34]
Pottier, C, Ravenscroft, TA, Brown, PH, et al. TYROBP genetic variants in early-onset Alzheimer’s disease[J]. Neurobiol Aging, 2016, 48:222.e229-222. e215.

[35]
Van Cauwenberghe, C,et al. Van Broeckhoven C, Sleegers K, The genetic landscape of Alzheimer disease: clinical implications and perspectives[J]. Genet Med, 2016, 18(5):421-430.

DOI PMID

[36]
Rosenthal, SL, Kamboh, MI, Late-onset Alzheimer’s disease genes and the potentially implicated pathways[J]. Curr Genet Med Rep, 2014, 2:85-101.

DOI

[37]
Ridge, PG, Ebbert, MT, Kauwe, JS, et al. Genetics of Alzheimer’s disease[J]. Biomed Res Int, 2013, 2013: 254954.

[38]
Karch, CM, Goate, AM, Alzheimer’s disease risk genes and mechanisms of disease pathogenesis[J]. Biol Psychiatry, 2015, 77(1):43-51.

DOI

[39]
Frisoni, GB, Pievani, M, Testa, C, et al. The topography of grey matter involvement in early and late onset Alzheimer’s disease[J]. Brain, 2007, 130(3):720-730.

DOI

[40]
Scarmeas, N, Stern, Y, Cognitive reserve: implications for diagnosis and prevention of Alzheimer’s disease[J]. Curr Neurol Neurosci Rep, 2004, 4(5):374-380.

DOI

[41]
Lo, RY, Jagust, WJ, Alzheimer’s disease neuroimaging I. effect of cognitive reserve markers on Alzheimer pathologic progression[J]. Alzheimer Dis Assoc Disord, 2013, 27(4):343-350.

DOI

[42]
Migliaccio, R, Agosta, F, Possin, KL, et al. Mapping the progression of atrophy in early- and late-onset Alzheimer’s disease[J]. J Alzheimers Dis, 2015, 46(2): 351-364.

DOI PMID

[43]
Canu, E, Frisoni, GB, Agosta, F, et al. Early and late onset Alzheimer’s disease patients have distinct patterns of white matter damage[J]. Neurobiol Aging, 2012, 33(6): 1023-1033.

DOI

[44]
Daianu, M, Mendez, MF, Baboyan, VG, et al. An advanced white matter tract analysis in frontotemporal dementia and early-onset Alzheimer’s disease[J]. Brain Imaging Behav, 2016, 10(4):1038-1053.

DOI

[45]
Acosta-Cabronero, J, Williams, GB, Pengas, G, et al. Absolute diffusivities define the landscape of white matter degeneration in Alzheimer’s disease[J]. Brain, 2010, 133(2):529-539.

DOI

[46]
Heun, R, Freymann, K, Erb, M, et al. Mild cognitive impairment (MCI) and actual retrieval performance affect cerebral activation in the elderly[J]. Neurobiol Aging, 2007, 28(3):404-413.

DOI

[47]
Garrido, GE, Furuie, SS, Buchpiguel, CA, et al. Relation between medial temporal atrophy and functional brain activity during memory processing in Alzheimer’s disease: a combined MRI and SPECT study[J]. J Neurol Neurosurg Psychiatry, 2002, 73(5):508-516.

DOI

[48]
Daianu, M, Mezher, A, Mendez, MF, et al. Disrupted rich club network in behavioral variant frontotemporal dementia and early-onset Alzheimer’s disease[J]. Hum Brain Mapp, 2016, 37(3):868-883.

DOI

[49]
Kim, EJ, Cho, SS, Jeong, Y, et al. Glucose metabolism in early onset versus late onset Alzheimer’s disease: an SPM analysis of 120 patients[J]. Brain, 2005, 128(8): 1790-1801.

DOI

[50]
Chiaravalloti, A, Koch, G, Toniolo, S, et al. Comparison between early-onset and late-onset Alzheimer’s disease patients with Amnestic presentation: CSF and (18)F-FDG PET study[J]. Dement Geriatr Cogn Dis Extra, 2016, 6(1):108-119.

DOI PMID

[51]
Shiino, A, Watanabe, T, Kitagawa, T, et al. Different atrophic patterns in early- and late-onset Alzheimer’s disease and evaluation of clinical utility of a method of regional z-score analysis using voxel-based morphometry[J]. Dement Geriatr Cogn Disord, 2008, 26 (2):175-186.

DOI

[52]
Kemp, PM, Holmes, C, Hoffmann, SM, et al. Alzheimer’s disease: differences in technetium-99m HMPAO SPECT scan findings between early onset and late onset dementia[J]. J Neurol Neurosurg Psychiatry, 2003, 74(6):715-719.

DOI

[53]
Chung, J, Yoo, K, Kim, E, et al. Glucose metabolic brain networks in early-onset vs. late-onset Alzheimer’s disease[J]. Front Aging Neurosci, 2016, 8:159.

[54]
Ballarini, T, Iaccarino, L, Magnani, G, et al. Neuropsychiatric subsyndromes and brain metabolic network dysfunctions in early onset Alzheimer’s disease[J]. Hum Brain Mapp, 2016, 37(12):4234-4247.

DOI PMID

[55]
Verclytte, S, Lopes, R, Lenfant, P, et al. Cerebral hypoperfusion and hypometabolism detected by arterial spin labeling MRI and FDG-PET in early-onset Alzheimer’s disease[J]. J Neuroimaging, 2016, 26(2):207-212.

DOI

[56]
van Rooden, S, Versluis, MJ, Liem, MK, et al. Cortical phase changes in Alzheimer’s disease at 7T MRI: a novel imaging marker[J]. Alzheimers Dement, 2014, 10 (1):e19-e26.

[57]
van Rooden, S, Doan, NT, Versluis, MJ, et al. 7T T(2)*-weighted magnetic resonance imaging reveals cortical phase differences between early- and late-onset Alzheimer’s disease[J]. Neurobiol Aging, 2015, 36(1):20-26.

DOI

[58]
Marshall, GA, Fairbanks, LA, Tekin, S, et al. Early-onset Alzheimer’s disease is associated with greater pathologic burden[J]. J Geriatr Psychiatry Neurol, 2007, 20(1):29-33.

DOI

[59]
Ho, GJ, Hansen, LA, Alford, MF, et al. Age at onset is associated with disease severity in Lewy body variant and Alzheimer’s disease[J]. Neuroreport, 2002, 13(14):1825-1828.

DOI

[60]
Ossenkoppele, R, Zwan, MD, Tolboom, N, et al. Amyloid burden and metabolic function in early-onset Alzheimer’s disease: parietal lobe involvement[J]. Brain, 2012, 135(7):2115-2125.

DOI

[61]
Cho, H, Seo, SW, Kim, JH, et al. Amyloid deposition in early onset versus late onset Alzheimer’s disease[J]. J Alzheimers Dis, 2013, 35(4):813-821.

DOI

[62]
Rabinovici, GD, Furst, AJ, Alkalay, A, et al. Increased metabolic vulnerability in early-onset Alzheimer’s disease is not related to amyloid burden[J]. Brain, 2010, 133(2):512-528.

DOI

[63]
Lehmann, M, Ghosh, PM, Madison, C, et al. Diverging patterns of amyloid deposition and hypometabolism in clinical variants of probable Alzheimer’s disease[J]. Brain, 2013, 136(3):844-858.

DOI

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