From the Department of Radiology, The MetroHealth System, 2500 MetroHealth Dr, Cleveland, OH 44109 (J.W.P.); Departments of Radiology (J.W.P., J.R.P.) and Psychiatry (P.M.D.), Duke University Medical Center, Durham, NC; Ruđer Bošković Institute, Zagreb, Croatia (D.G.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (T.B.).
Radiology. 2022 Jan;302(1):143-150. doi: 10.1148/radiol.2021210383. Epub 2021 Oct 12.
Background Pathologic evidence of Alzheimer disease (AD) is detectable years before onset of clinical symptoms. Imaging-based identification of structural changes of the brain in people at genetic risk for early-onset AD may provide insights into how genes influence the pathologic cascade that leads to dementia. Purpose To assess structural connectivity differences in cortical networks between cognitively normal autosomal dominant Alzheimer disease (ADAD) mutation carriers versus noncarriers and to determine the cross-sectional relationship of structural connectivity and cortical amyloid burden with estimated years to symptom onset (EYO) of dementia in carriers. Materials and Methods In this exploratory analysis of a prospective trial, all participants enrolled in the Dominantly Inherited Alzheimer Network between January 2009 and July 2014 who had normal cognition at baseline, T1-weighted MRI scans, and diffusion tensor imaging (DTI) were analyzed. Amyloid PET imaging using Pittsburgh compound B was also analyzed for mutation carriers. Areas of the cerebral cortex were parcellated into three cortical networks: the default mode network, frontoparietal control network, and ventral attention network. The structural connectivity of the three networks was calculated from DTI. General linear models were used to examine differences in structural connectivity between mutation carriers and noncarriers and the relationship between structural connectivity, amyloid burden, and EYO in mutation carriers. Correlation network analysis was performed to identify clusters of related clinical and imaging markers. Results There were 30 mutation carriers (mean age ± standard deviation, 34 years ± 10; 17 women) and 38 noncarriers (mean age, 37 years ± 10; 20 women). There was lower structural connectivity in the frontoparietal control network in mutation carriers compared with noncarriers (estimated effect of mutation-positive status, -0.0266; = .04). Among mutation carriers, there was a correlation between EYO and white matter structural connectivity in the frontoparietal control network (estimated effect of EYO, -0.0015, = .01). There was no significant relationship between cortical global amyloid burden and EYO among mutation carriers ( > .05). Conclusion White matter structural connectivity was lower in autosomal dominant Alzheimer disease mutation carriers compared with noncarriers and correlated with estimated years to symptom onset. Clinical trial registration no. NCT00869817 © RSNA, 2021 See also the editorial by McEvoy in this issue.
背景 阿尔茨海默病（AD）的病理证据在临床症状出现前数年即可检测到。在具有早发性 AD 遗传风险的人群中，基于影像学的大脑结构变化识别可能会深入了解基因如何影响导致痴呆的病理级联。目的 评估认知正常的常染色体显性 AD（ADAD）突变携带者与非携带者之间皮质网络结构连接的差异，并确定结构连接和皮质淀粉样蛋白负荷与携带者出现痴呆症状的估计时间（EYO）的横断面关系。材料与方法 这是一项前瞻性试验的探索性分析，对 2009 年 1 月至 2014 年 7 月期间所有在基线时认知正常、有 T1 加权 MRI 扫描和弥散张量成像（DTI）且有 AD 家族遗传史的参与者进行了分析。还对突变携带者进行了匹兹堡化合物 B 的淀粉样 PET 成像分析。将大脑皮质划分为三个皮质网络：默认模式网络、额顶控制网络和腹侧注意网络。从 DTI 计算三个网络的结构连接。使用一般线性模型来检查突变携带者和非携带者之间结构连接的差异，以及突变携带者的结构连接、淀粉样蛋白负荷与 EYO 之间的关系。进行相关网络分析以识别相关临床和影像学标志物的聚类。结果 有 30 名突变携带者（平均年龄 ± 标准差，34 岁 ± 10 岁；17 名女性）和 38 名非携带者（平均年龄，37 岁 ± 10 岁；20 名女性）。与非携带者相比，突变携带者的额顶控制网络结构连接较低（突变阳性状态的估计效应，-0.0266； =.04）。在突变携带者中，额顶控制网络的 EYO 与白质结构连接之间存在相关性（EYO 的估计效应，-0.0015， =.01）。在突变携带者中，皮质整体淀粉样蛋白负荷与 EYO 之间无显著相关性（ >.05）。结论 与非携带者相比，常染色体显性 AD 突变携带者的白质结构连接较低，与估计的发病时间相关。临床试验注册号 NCT00869817 © RSNA，2021 参见本期 McEvoy 的社论。
