Wearn Alfie, Raket Lars Lau, Collins D Louis, Spreng R Nathan
Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada H3A 2B4.
Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund SE-221 00, Sweden.
Brain Commun. 2023 Jul 5;5(4):fcad195. doi: 10.1093/braincomms/fcad195. eCollection 2023.
Early detection of Alzheimer's disease is essential to develop preventive treatment strategies. Detectible change in brain volume emerges relatively late in the pathogenic progression of disease, but microstructural changes caused by early neuropathology may cause subtle changes in the MR signal, quantifiable using texture analysis. Texture analysis quantifies spatial patterns in an image, such as smoothness, randomness and heterogeneity. We investigated whether the MRI texture of the hippocampus, an early site of Alzheimer's disease pathology, is sensitive to changes in brain microstructure before the onset of cognitive impairment. We also explored the longitudinal trajectories of hippocampal texture across the Alzheimer's continuum in relation to hippocampal volume and other biomarkers. Finally, we assessed the ability of texture to predict future cognitive decline, over and above hippocampal volume. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative. Texture was calculated for bilateral hippocampi on 3T T-weighted MRI scans. Two hundred and ninety-three texture features were reduced to five principal components that described 88% of total variance within cognitively unimpaired participants. We assessed cross-sectional differences in these texture components and hippocampal volume between four diagnostic groups: cognitively unimpaired amyloid-β ( = 406); cognitively unimpaired amyloid-β ( = 213); mild cognitive impairment amyloid-β ( = 347); and Alzheimer's disease dementia amyloid-β ( = 202). To assess longitudinal texture change across the Alzheimer's continuum, we used a multivariate mixed-effects spline model to calculate a 'disease time' for all timepoints based on amyloid PET and cognitive scores. This was used as a scale on which to compare the trajectories of biomarkers, including volume and texture of the hippocampus. The trajectories were modelled in a subset of the data: cognitively unimpaired amyloid-β ( = 345); cognitively unimpaired amyloid-β ( = 173); mild cognitive impairment amyloid-β ( = 301); and Alzheimer's disease dementia amyloid-β ( = 161). We identified a difference in texture component 4 at the earliest stage of Alzheimer's disease, between cognitively unimpaired amyloid-β and cognitively unimpaired amyloid-β older adults (Cohen's = 0.23, = 0.014). Differences in additional texture components and hippocampal volume emerged later in the disease continuum alongside the onset of cognitive impairment ( = 0.30-1.22, < 0.002). Longitudinal modelling of the texture trajectories revealed that, while most elements of texture developed over the course of the disease, noise reduced sensitivity for tracking individual textural change over time. Critically, however, texture provided additional information than was provided by volume alone to more accurately predict future cognitive change ( = 0.32-0.63, < 0.0001). Our results support the use of texture as a measure of brain health, sensitive to Alzheimer's disease pathology, at a time when therapeutic intervention may be most effective.
早期发现阿尔茨海默病对于制定预防性治疗策略至关重要。脑容量的可检测变化在疾病的致病进展过程中出现得相对较晚,但早期神经病理学引起的微观结构变化可能会导致磁共振信号的细微变化,可通过纹理分析进行量化。纹理分析可量化图像中的空间模式,如平滑度、随机性和异质性。我们研究了海马体(阿尔茨海默病病理学的早期部位)的磁共振成像纹理在认知障碍发作前是否对脑微观结构的变化敏感。我们还探讨了海马体纹理在阿尔茨海默病连续体中的纵向轨迹与海马体体积和其他生物标志物的关系。最后,我们评估了纹理在预测未来认知衰退方面的能力,超越了海马体体积。数据来自阿尔茨海默病神经成像计划。在3T T加权磁共振成像扫描上计算双侧海马体的纹理。293个纹理特征被缩减为五个主成分,这些主成分描述了认知未受损参与者中总方差的88%。我们评估了四个诊断组之间这些纹理成分和海马体体积的横断面差异:认知未受损的淀粉样蛋白β(=406);认知未受损的淀粉样蛋白β(=213);轻度认知障碍淀粉样蛋白β(=347);以及阿尔茨海默病痴呆淀粉样蛋白β(=202)。为了评估阿尔茨海默病连续体中海马体纹理的纵向变化,我们使用多元混合效应样条模型根据淀粉样蛋白PET和认知评分计算所有时间点的“疾病时间”。这被用作一个尺度,用于比较生物标志物的轨迹,包括海马体的体积和纹理。在数据的一个子集中对轨迹进行建模:认知未受损的淀粉样蛋白β(=345);认知未受损的淀粉样蛋白β(=173);轻度认知障碍淀粉样蛋白β(=301);以及阿尔茨海默病痴呆淀粉样蛋白β(=161)。我们在阿尔茨海默病的最早阶段发现了纹理成分4的差异,在认知未受损的淀粉样蛋白β和认知未受损的老年淀粉样蛋白β之间(科恩d=0.23,P=0.014)。在疾病连续体中,随着认知障碍的出现,其他纹理成分和海马体体积的差异在疾病后期出现(d=0.30 - 1.22,P<0.002)。纹理轨迹的纵向建模显示,虽然纹理的大多数元素在疾病过程中发展,但噪声降低了随时间跟踪个体纹理变化的敏感性。然而,至关重要的是,纹理提供了比单独体积更多的信息,以更准确地预测未来的认知变化(d=0.32 - 0.63,P<0.0001)。我们的结果支持在治疗干预可能最有效的时候,将纹理用作衡量脑健康的指标,对阿尔茨海默病病理学敏感。