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Preferential degradation of cognitive networks differentiates Alzheimer's disease from ageing.
Brain ( IF 10.6 ) Pub Date : 2018-05-01 , DOI: 10.1093/brain/awy053
Jasmeer P Chhatwal 1, 2, 3 , Aaron P Schultz 1, 2 , Keith A Johnson 1, 2, 3, 4 , Trey Hedden 2, 4 , Sehily Jaimes 1 , Tammie L S Benzinger 5, 6 , Clifford Jack 7 , Beau M Ances 6, 8 , John M Ringman 9 , Daniel S Marcus 5, 6 , Bernardino Ghetti 10 , Martin R Farlow 11 , Adrian Danek 12, 13 , Johannes Levin 13, 14 , Igor Yakushev 13, 15 , Christoph Laske 14, 16 , Robert A Koeppe 17 , Douglas R Galasko 18 , Chengjie Xiong 19 , Colin L Masters 20 , Peter R Schofield 21, 22 , Kirsi M Kinnunen 23 , Stephen Salloway 24, 25 , Ralph N Martins 26 , Eric McDade 8 , Nigel J Cairns 8 , Virginia D Buckles 8 , John C Morris 8 , Randall Bateman 8 , Reisa A Sperling 1, 2, 3 ,
Affiliation  

Converging evidence from structural, metabolic and functional connectivity MRI suggests that neurodegenerative diseases, such as Alzheimer's disease, target specific neural networks. However, age-related network changes commonly co-occur with neuropathological cascades, limiting efforts to disentangle disease-specific alterations in network function from those associated with normal ageing. Here we elucidate the differential effects of ageing and Alzheimer's disease pathology through simultaneous analyses of two functional connectivity MRI datasets: (i) young participants harbouring highly-penetrant mutations leading to autosomal-dominant Alzheimer's disease from the Dominantly Inherited Alzheimer's Network (DIAN), an Alzheimer's disease cohort in which age-related comorbidities are minimal and likelihood of progression along an Alzheimer's disease trajectory is extremely high; and (ii) young and elderly participants from the Harvard Aging Brain Study, a cohort in which imaging biomarkers of amyloid burden and neurodegeneration can be used to disambiguate ageing alone from preclinical Alzheimer's disease. Consonant with prior reports, we observed the preferential degradation of cognitive (especially the default and dorsal attention networks) over motor and sensory networks in early autosomal-dominant Alzheimer's disease, and found that this distinctive degradation pattern was magnified in more advanced stages of disease. Importantly, a nascent form of the pattern observed across the autosomal-dominant Alzheimer's disease spectrum was also detectable in clinically normal elderly with clear biomarker evidence of Alzheimer's disease pathology (preclinical Alzheimer's disease). At the more granular level of individual connections between node pairs, we observed that connections within cognitive networks were preferentially targeted in Alzheimer's disease (with between network connections relatively spared), and that connections between positively coupled nodes (correlations) were preferentially degraded as compared to connections between negatively coupled nodes (anti-correlations). In contrast, ageing in the absence of Alzheimer's disease biomarkers was characterized by a far less network-specific degradation across cognitive and sensory networks, of between- and within-network connections, and of connections between positively and negatively coupled nodes. We go on to demonstrate that formalizing the differential patterns of network degradation in ageing and Alzheimer's disease may have the practical benefit of yielding connectivity measurements that highlight early Alzheimer's disease-related connectivity changes over those due to age-related processes. Together, the contrasting patterns of connectivity in Alzheimer's disease and ageing add to prior work arguing against Alzheimer's disease as a form of accelerated ageing, and suggest multi-network composite functional connectivity MRI metrics may be useful in the detection of early Alzheimer's disease-specific alterations co-occurring with age-related connectivity changes. More broadly, our findings are consistent with a specific pattern of network degradation associated with the spreading of Alzheimer's disease pathology within targeted neural networks.

中文翻译:

认知网络的优先降级可将老年痴呆症与衰老区分开。

来自结构,代谢和功能连接性MRI的越来越多的证据表明,神经退行性疾病(例如阿尔茨海默氏病)以特定的神经网络为目标。但是,与年龄相关的网络变化通常与神经病理学级联同时发生,从而限制了将疾病特定的网络功能改变与正常衰老相关的努力区分开的努力。在这里,我们通过同时分析两个功能连通性MRI数据集阐明了衰老和阿尔茨海默氏病病理学的不同影响:(i)年轻参与者具有从显性遗传的阿尔茨海默氏病网络(DIAN)导致常染色体显性阿尔茨海默氏病的高渗透性突变老年痴呆症 与年龄相关的合并症极少且沿着阿尔茨海默氏病轨迹发展的可能性极高的疾病队列;(ii)哈佛大学老化大脑研究小组的年轻人和老年人,该研究利用淀粉样蛋白负荷和神经退行性变的生物标志物,可以单独消除衰老和临床前阿尔茨海默氏病。与先前的报道相一致,我们观察到在常染色体显性遗传的早期阿尔茨海默氏病中,认知(尤其是默认的和背注意力的网络)优先于运动和感觉网络的退化,并且发现这种独特的退化模式在疾病的更晚期阶段被放大。重要的是,在常染色体显性遗传的阿尔茨海默氏病中观察到的是这种模式的新生形式 在临床上正常的老年人中也可检测到该病的谱,并具有明显的阿尔茨海默氏病病理学(临床前阿尔茨海默氏病)的生物标志物证据。在节点对之间的各个连接的更细粒度的层次上,我们观察到认知网络内的连接优先针对阿尔茨海默氏病(相对而言,网络连接之间没有关联),并且与之相比,正耦合节点之间的连接(相关性)优先降级。负耦合节点之间的连接(反相关)。相比之下,在缺乏阿尔茨海默氏病生物标记物的情况下,衰老的特征在于,跨认知和感觉网络,网络间和网络内连接的网络特定退化程度要小得多,正耦合节点和负耦合节点之间的连接。我们继续证明,对衰老和阿尔茨海默氏病网络退化的差异模式进行正规化可能具有产生连通性测量的实际好处,该连通性测量结果强调了与年龄相关的过程相比,早期与阿尔茨海默氏病相关的连通性变化。总之,阿尔茨海默氏病和衰老的连通性形成了鲜明对比,这加剧了先前针对阿尔茨海默氏病作为加速衰老的形式的争论,并且表明多网络复合功能连通性MRI指标可能对检测早期阿尔茨海默氏症特定疾病的变化有用与年龄相关的连接性变化同时发生。更广泛地,
更新日期:2018-03-07
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