Winter Steven, Mahzarnia Ali, Anderson Robert J, Han Zay Yar, Tremblay Jessica, Stout Jacques, Moon Hae Sol, Marcellino Daniel, Dunson David B, Badea Alexandra
Statistical Science, Trinity School, Duke University, Durham, NC, 27710 USA.
Department of Radiology, Duke University School of Medicine. Durham, NC, 27710. USA.
bioRxiv. 2024 Jul 1:2023.10.04.560954. doi: 10.1101/2023.10.04.560954.
Alzheimer's disease (AD) presents complex challenges due to its multifactorial nature, poorly understood etiology, and late detection. The mechanisms through which genetic, fixed and modifiable risk factors influence susceptibility to AD are under intense investigation, yet the impact of unique risk factors on brain networks is difficult to disentangle, and their interactions remain unclear. To model multiple risk factors including APOE genotype, age, sex, diet, and immunity we leveraged mice expressing the human APOE and NOS2 genes, conferring a reduced immune response compared to mouse Nos2. Employing graph analyses of brain connectomes derived from accelerated diffusion-weighted MRI, we assessed the global and local impact of risk factors in the absence of AD pathology. Aging and a high-fat diet impacted extensive networks comprising AD-vulnerable regions, including the temporal association cortex, amygdala, and the periaqueductal gray, involved in stress responses. Sex impacted networks including sexually dimorphic regions (thalamus, insula, hypothalamus) and key memory-processing areas (fimbria, septum). APOE genotypes modulated connectivity in memory, sensory, and motor regions, while diet and immunity both impacted the insula and hypothalamus. Notably, these risk factors converged on a circuit comprising 63 of 54,946 total connections (0.11% of the connectome), highlighting shared vulnerability amongst multiple AD risk factors in regions essential for sensory integration, emotional regulation, decision making, motor coordination, memory, homeostasis, and interoception. These network-based biomarkers hold translational value for distinguishing high-risk versus low-risk participants at preclinical AD stages, suggest circuits as potential therapeutic targets, and advance our understanding of network fingerprints associated with AD risk.
Current interventions for Alzheimer's disease (AD) do not provide a cure, and are delivered years after neuropathological onset. Addressing the impact of risk factors on brain networks holds promises for early detection, prevention, and revealing putative therapeutic targets at preclinical stages. We utilized six mouse models to investigate the impact of factors, including APOE genotype, age, sex, immunity, and diet, on brain networks. Large structural connectomes were derived from high resolution compressed sensing diffusion MRI. A highly parallelized graph classification identified subnetworks associated with unique risk factors, revealing their network fingerprints, and a common network composed of 63 connections with shared vulnerability to all risk factors. APOE genotype specific immune signatures support the design of interventions tailored to risk profiles.
阿尔茨海默病(AD)因其多因素性质、病因理解不足以及检测较晚而带来复杂挑战。遗传、固定和可改变的风险因素影响AD易感性的机制正在深入研究中,但独特风险因素对脑网络的影响难以厘清,且它们之间的相互作用仍不明确。为了对包括APOE基因型、年龄、性别、饮食和免疫等多种风险因素进行建模,我们利用了表达人类APOE和NOS2基因的小鼠,与小鼠Nos2相比,其免疫反应降低。通过对源自加速扩散加权MRI的脑连接组进行图谱分析,我们评估了在没有AD病理情况下风险因素的全局和局部影响。衰老和高脂饮食影响了包括AD易损区域(如颞叶联合皮质、杏仁核和导水管周围灰质)在内的广泛网络,这些区域参与应激反应。性别影响的网络包括性二态区域(丘脑、岛叶、下丘脑)和关键记忆处理区域(穹窿、隔区)。APOE基因型调节记忆、感觉和运动区域的连接,而饮食和免疫均影响岛叶和下丘脑。值得注意的是,这些风险因素汇聚在一个由总共54946个连接中的63个组成的回路中(占连接组的0.11%),突出了在感觉整合、情绪调节、决策、运动协调、记忆、内环境稳定和内感受等关键区域中多种AD风险因素之间的共同易损性。这些基于网络的生物标志物在区分临床前AD阶段的高风险和低风险参与者方面具有转化价值,提示回路作为潜在治疗靶点,并推进了我们对与AD风险相关的网络指纹的理解。
目前针对阿尔茨海默病(AD)的干预措施无法治愈疾病,且在神经病理学发病数年后才实施。解决风险因素对脑网络的影响有望实现早期检测、预防,并在临床前阶段揭示假定的治疗靶点。我们利用六种小鼠模型研究了包括APOE基因型、年龄、性别、免疫和饮食等因素对脑网络的影响。大尺度结构连接组源自高分辨率压缩感知扩散MRI。一种高度并行化的图谱分类识别出与独特风险因素相关的子网络,揭示了它们的网络指纹,以及一个由63个连接组成的共同网络,该网络对所有风险因素具有共同易损性。APOE基因型特异性免疫特征支持针对风险特征量身定制干预措施的设计。