1 Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin.
2 Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
Brain Connect. 2019 Mar;9(2):162-173. doi: 10.1089/brain.2018.0590. Epub 2019 Jan 7.
Characterizing Alzheimer's disease (AD) at pre-clinical stages is crucial for initiating early treatment strategies. It is widely accepted that amyloid accumulation is a primary pathological event in AD. Also, loss of connectivity between brain regions is suspected of contributing to cognitive decline, but studies that test these associations using either local (i.e., individual edges) or global (i.e., modularity) connectivity measures may be limited. In this study, we utilized data acquired from 139 cognitively unimpaired participants. Sixteen gray matter (GM) regions known to be affected by AD were selected for analysis. For each of the 16 regions, the effect of amyloid burden, measured using Pittsburgh Compound B (PiB) positron emission tomography, on each of the 1761 brain network connections derived from diffusion tensor imaging (DTI) connecting 162 GM regions, was investigated. Applying our unique multiresolution statistical analysis called the Wavelet Connectivity Signature (WaCS), this study demonstrates the relationship between amyloid burden and structural brain connectivity as assessed with DTI. Our statistical analysis using WaCS shows that in 15 of 16 GM regions, statistically significant relationships between amyloid burden in those regions and structural connectivity networks were observed. After applying multiple testing correction, 10 unique structural brain connections were found to be significantly associated with amyloid accumulation. For 7 of those 10 network connections, the decrease in their network connection strength indexed by fractional anisotropy was, in turn, associated with lower cognitive function, providing evidence that AD-related structural connectivity loss is a correlate of cognitive decline.
在临床前阶段对阿尔茨海默病(AD)进行特征描述对于启动早期治疗策略至关重要。人们普遍认为,淀粉样蛋白的积累是 AD 的主要病理事件。此外,大脑区域之间的连接丧失被怀疑导致认知能力下降,但使用局部(即,个体边缘)或全局(即,模块性)连接测量来测试这些关联的研究可能存在局限性。在这项研究中,我们利用从 139 名认知正常的参与者中获得的数据。选择了 16 个已知受 AD 影响的灰质(GM)区域进行分析。对于这 16 个区域中的每一个,使用正电子发射断层扫描(PiB)来测量淀粉样蛋白负荷,对来自扩散张量成像(DTI)的 162 个 GM 区域连接的 1761 个脑网络连接中的每一个的影响都进行了研究。应用我们独特的称为小波连接特征(WaCS)的多分辨率统计分析,这项研究表明了淀粉样蛋白负荷与用 DTI 评估的结构脑连接之间的关系。我们使用 WaCS 的统计分析表明,在 16 个 GM 区域中的 15 个区域中,观察到这些区域中的淀粉样蛋白负荷与结构连接网络之间存在统计学显著的关系。在应用多重测试校正后,发现 10 个独特的结构脑连接与淀粉样蛋白积累显著相关。在这 10 个网络连接中的 7 个中,其网络连接强度(以各向异性分数表示)的降低与较低的认知功能相关,这提供了证据表明与 AD 相关的结构连接丧失是认知能力下降的一个指标。