神经科学
骨质疏松症
认知
病态的
疾病
认知功能衰退
生物信息学
医学
神经影像学
默认模式网络
机制(生物学)
骨重建
骨矿物
心理学
功能成像
骨吸收
构造(python库)
代谢性骨病
计算机科学
骨病
生物标志物
干预(咨询)
骨密度
生物
计算生物学
认知障碍
骨形成
作者
Yunhai Mao,Zhe Sui,M Zhang,Y M
标识
DOI:10.3389/fnagi.2026.1799025
摘要
Against the backdrop of an accelerating global aging population, the epidemiological correlation between cognitive impairment and osteoporosis has become increasingly prominent. These two conditions exhibit a profound pathological coupling mediated by the bidirectional regulatory network of the "bone-brain axis." The operation of this axis is rooted in an intricate neuro-skeletal signaling network involving hormonal dysregulation, systemic inflammatory cascades, and the aberrant regulation of core molecular pathways, such as Wnt/β-catenin and RANKL/OPG. Together, these factors synergistically drive the synchronized pathological progression of enhanced bone resorption and neurodegeneration. To address these complex pathological interactions, clinical evaluation strategies are undergoing a paradigm shift, transitioning from single-modality assessments toward the deep integration of multimodal imaging. By fusing cutting-edge technologies-including structural/functional MRI, molecular PET imaging (targeting Aβ and Tau deposition), and high-resolution peripheral quantitative computed tomography (HR-pQCT)-researchers can now comprehensively characterize the spatiotemporal patterns of bone microstructural degradation and brain functional evolution across scales ranging from the microscopic to the macroscopic. Prospectively, leveraging deep learning algorithms such as 3D-CNN to integrate multimodal biomarkers and construct risk-prediction models for bone-brain comorbidities will emerge as a pivotal pathway for achieving early precision screening and personalized preventive intervention for Alzheimer's disease (AD).
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