Longitudinal changes in hippocampal texture from healthy aging to Alzheimer's disease.
Longitudinal changes in hippocampal texture from healthy aging to Alzheimer's disease.
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DOI：
10.1093/braincomms/fcad195
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发表时间：
2023
影响因子：
4.8
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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。可检测的脑体积变化出现相对较晚的疾病的病理进展，但由早期神经病理学引起的微结构变化可能会导致微妙的变化，在MR信号，量化使用纹理分析。纹理分析量化图像中的空间模式，例如平滑度、随机性和异质性。我们研究了阿尔茨海默病病理学的早期部位海马的MRI纹理是否对认知障碍发作前大脑微结构的变化敏感。我们还探讨了纵向轨迹的海马纹理在整个阿尔茨海默氏症的连续海马体积和其他生物标志物。最后，我们评估了纹理预测未来认知能力下降的能力，超过海马体积。数据来自阿尔茨海默病神经成像倡议。在3 T T1加权MRI扫描上计算双侧腰肌的纹理。293纹理特征减少到五个主成分，描述了88%的认知未受损的参与者内的总方差。我们评估了四个诊断组之间这些纹理成分和海马体积的横截面差异：认知未受损淀粉样蛋白-β−（n = 406）;认知未受损淀粉样蛋白-β+（n = 213）;轻度认知障碍淀粉样蛋白-β+（n = 347）;阿尔茨海默病痴呆淀粉样蛋白-β+（n = 202）。为了评估阿尔茨海默氏症连续体的纵向纹理变化，我们使用多变量混合效应样条模型来计算基于淀粉样蛋白PET和认知评分的所有时间点的“疾病时间”。这被用作比较生物标志物的轨迹的尺度，包括海马体的体积和纹理。这些轨迹是在一个数据子集中建模的：认知未受损的淀粉样蛋白-β-（n = 345）;认知未受损的淀粉样蛋白-β+（n = 173）;轻度认知障碍淀粉样蛋白-β+（n = 301）;和阿尔茨海默病痴呆淀粉样蛋白-β+（n = 161）。我们发现，在阿尔茨海默病的早期阶段，认知未受损的淀粉样蛋白-β−和认知未受损的淀粉样蛋白-β+老年人之间的纹理成分4存在差异（Cohen d = 0.23，Padj = 0.014）。随着认知障碍的发生，在疾病连续体中出现了额外纹理成分和海马体积的差异（d = 0.30-1.22，Padj < 0.002）。纹理轨迹的纵向建模显示，虽然大多数纹理元素在疾病过程中发展，但噪声降低了跟踪个人纹理变化的灵敏度。然而，重要的是，纹理提供了比单独的体积提供的更多的信息，以更准确地预测未来的认知变化（d = 0.32-0.63，Padj < 0.0001）。我们的研究结果支持使用纹理作为衡量大脑健康的指标，对阿尔茨海默病的病理学敏感，在治疗干预可能最有效的时候。Wearn等人表明，海马体的纹理分析可以识别认知未受损的高危老年人中阿尔茨海默病病理学的迹象，并且可以在海马体体积的差异可检测之前这样做。海马纹理分析可以从现有的临床MRI管道中提取额外的信息，以改善患者的预后。
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 T1-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− (n = 406); cognitively unimpaired amyloid-β+ (n = 213); mild cognitive impairment amyloid-β+ (n = 347); and Alzheimer’s disease dementia amyloid-β+ (n = 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− (n = 345); cognitively unimpaired amyloid-β+ (n = 173); mild cognitive impairment amyloid-β+ (n = 301); and Alzheimer’s disease dementia amyloid-β+ (n = 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 d = 0.23, Padj = 0.014). Differences in additional texture components and hippocampal volume emerged later in the disease continuum alongside the onset of cognitive impairment (d = 0.30–1.22, Padj < 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 (d = 0.32–0.63, Padj < 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. Wearn et al. show that texture analysis of the hippocampus can identify signs of Alzheimer’s disease pathology in cognitively unimpaired at-risk older adults, and can do so before differences in hippocampal volume are detectible. Hippocampal texture analysis can extract additional information from existing clinical MRI pipelines to improve patient prognosis.