Prediction of Cognitive Progression Due to Alzheimer's Disease in Normal Participants Based on Individual Default Mode Network Metabolic Connectivity Strength
Qi Zhang , Fangjie Li , Min Wei , Min Wang , Luyao Wang , Ying Han , Jiehui Jiang , Alzheimer’s Disease Neuroimaging Initiative
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引用次数: 0
Abstract
Background
Predicting cognitive decline among individuals in the aging population who are already amyloid-β (Aβ) positive or tau positive poses clinical challenges. In Alzheimer's disease research, intra–default mode network (DMN) connections play a pivotal role in diagnosis. In this article, we propose metabolic connectivity within the DMN as a supplementary biomarker to the Aβ, pathological tau, and neurodegeneration framework.
Methods
Extracting data from 1292 participants in the Alzheimer’s Disease Neuroimaging Initiative, we collected paired T1-weighted structural magnetic resonance imaging and 18F-labeled-fluorodeoxyglucose positron emission computed tomography scans. Individual metabolic DMN networks were constructed, and metabolic connectivity (MC) strength in the DMN was assessed. In the cognitively unimpaired group, the Cox model identified cognitively unimpaired (MC+), high-risk participants, with Kaplan-Meier survival analyses and hazard ratios revealing the strength of MC’s predictive performance. Spearman correlation analyses explored relationships between MC strength, and Aβ, pathological tau, neurodegeneration biomarkers, and clinical scales. DMN standard uptake value ratio (SUVR) provided comparative insights in the analyses.
Results
Both MC strength and SUVR exhibited gradual declines with cognitive deterioration, displaying significant intergroup differences. Survival analyses indicated enhanced Aβ and tau prediction with both metrics, with MC strength outperforming SUVR. Combined MC strength and Aβ yielded optimal predictive performance (hazard ratio = 9.29), followed by MC strength and tau (hazard ratio = 8.92). Generally, the strength of MC’s correlations with Aβ, pathological tau, and neurodegeneration biomarkers exceeded SUVR.
Conclusions
Individuals with normal cognition and disrupted DMN metabolic connectivity face an elevated risk of cognitive decline linked to Aβ that precedes metabolic issues.
背景预测已出现淀粉样蛋白-β(Aβ)阳性或tau阳性的老龄人口的认知能力下降是一项临床挑战。在阿尔茨海默病的研究中,默认模式网络(DMN)内的连接在诊断中起着举足轻重的作用。在本文中,我们提出将DMN内的代谢连接作为Aβ、病理tau和神经变性框架的补充生物标志物。方法我们从阿尔茨海默病神经影像倡议的1292名参与者中提取数据,收集了配对的T1加权结构磁共振成像和18F标记的氟脱氧葡萄糖正电子发射计算机断层扫描。我们构建了单个代谢DMN网络,并评估了DMN的代谢连通性(MC)强度。在认知功能未受损组中,Cox模型确定了认知功能未受损(MC+)的高风险参与者,Kaplan-Meier生存分析和危险比揭示了MC的预测能力。斯皮尔曼相关性分析探讨了MC强度与Aβ、病理tau、神经变性生物标记物和临床量表之间的关系。结果MC强度和SUVR都随着认知能力的退化而逐渐下降,并显示出显著的组间差异。生存分析表明,这两种指标都能增强对 Aβ 和 tau 的预测,其中 MC 强度优于 SUVR。将 MC 强度和 Aβ 结合使用可获得最佳预测效果(危险比 = 9.29),其次是 MC 强度和 tau(危险比 = 8.92)。一般来说,MC 与 Aβ、病理 tau 和神经变性生物标志物的相关性强度超过 SUVR。
期刊介绍:
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging is an official journal of the Society for Biological Psychiatry, whose purpose is to promote excellence in scientific research and education in fields that investigate the nature, causes, mechanisms, and treatments of disorders of thought, emotion, or behavior. In accord with this mission, this peer-reviewed, rapid-publication, international journal focuses on studies using the tools and constructs of cognitive neuroscience, including the full range of non-invasive neuroimaging and human extra- and intracranial physiological recording methodologies. It publishes both basic and clinical studies, including those that incorporate genetic data, pharmacological challenges, and computational modeling approaches. The journal publishes novel results of original research which represent an important new lead or significant impact on the field. Reviews and commentaries that focus on topics of current research and interest are also encouraged.