PDF(Pubmed)
Abstract：
BACKGROUND: Brain age model, including estimated brain age and brain-predicted age difference (brain-PAD), has shown great potentials for serving as imaging markers for monitoring normal ageing, as well as for identifying the individuals in the pre-diagnostic phase of neurodegenerative diseases.
OBJECTIVE: This study aimed to investigate the brain age models in normal ageing and mild cognitive impairments (MCI) converters and their values in classifying MCI conversion.
METHODS: Pre-trained brain age model was constructed using the structural magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) project (N = 609). The tested brain age model was built using the baseline, 1-year and 3-year follow-up MRI data from normal ageing (NA) adults (n = 32) and MCI converters (n = 22) drew from the Open Access Series of Imaging Studies (OASIS-2). The quantitative measures of morphometry included total intracranial volume (TIV), gray matter volume (GMV) and cortical thickness. Brain age models were calculated based on the individual\'s morphometric features using the support vector machine (SVM) algorithm.
RESULTS: With comparable chronological age, MCI converters showed significant increased TIV-based (Baseline: P = 0.021; 1-year follow-up: P = 0.037; 3-year follow-up: P = 0.001) and left GMV-based brain age than NA adults at all time points. Higher brain-PAD scores were associated with worse global cognition. Acceptable classification performance of TIV-based (AUC = 0.698) and left GMV-based brain age (AUC = 0.703) was found, which could differentiate the MCI converters from NA adults at the baseline.
CONCLUSIONS: This is the first demonstration that MRI-informed brain age models exhibit feature-specific patterns. The greater GMV-based brain age observed in MCI converters may provide new evidence for identifying the individuals at the early stage of neurodegeneration. Our findings added value to existing quantitative imaging markers and might help to improve disease monitoring and accelerate personalized treatments in clinical practice.
Based on individual’s MRI scans, brain age model has shown great potentials for serving as imaging markers for monitoring normal ageing (NA), as well as for identifying the ones in the pre-diagnostic phase of age-related neurodegenerative diseases. In this study, we investigated the brain age models in normal ageing and mild cognitive impairments (MCI) converters and their values in classifying MCI conversion. Pre-trained brain age model was constructed using the quantitative measures of morphometry included total intracranial volume (TIV), gray matter volume (GMV) and cortical thickness. With comparable chronological age, MCI converters showed significant increased brain age than NA adults at all time points. Higher brain age were associated with worse global cognition. This is the first demonstration that MRI-informed brain age models exhibit feature-specific patterns. The greater GMV-based brain age observed in MCI converters may provide new evidence for identifying the individuals at the early stage of neurodegeneration. Our findings added value to existing quantitative imaging markers and might help to improve disease monitoring and accelerate personalized treatments in clinical practice.
OBJECTIVE: This study aimed to investigate the brain age models in normal ageing and mild cognitive impairments (MCI) converters and their values in classifying MCI conversion.
METHODS: Pre-trained brain age model was constructed using the structural magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) project (N = 609). The tested brain age model was built using the baseline, 1-year and 3-year follow-up MRI data from normal ageing (NA) adults (n = 32) and MCI converters (n = 22) drew from the Open Access Series of Imaging Studies (OASIS-2). The quantitative measures of morphometry included total intracranial volume (TIV), gray matter volume (GMV) and cortical thickness. Brain age models were calculated based on the individual\'s morphometric features using the support vector machine (SVM) algorithm.
RESULTS: With comparable chronological age, MCI converters showed significant increased TIV-based (Baseline: P = 0.021; 1-year follow-up: P = 0.037; 3-year follow-up: P = 0.001) and left GMV-based brain age than NA adults at all time points. Higher brain-PAD scores were associated with worse global cognition. Acceptable classification performance of TIV-based (AUC = 0.698) and left GMV-based brain age (AUC = 0.703) was found, which could differentiate the MCI converters from NA adults at the baseline.
CONCLUSIONS: This is the first demonstration that MRI-informed brain age models exhibit feature-specific patterns. The greater GMV-based brain age observed in MCI converters may provide new evidence for identifying the individuals at the early stage of neurodegeneration. Our findings added value to existing quantitative imaging markers and might help to improve disease monitoring and accelerate personalized treatments in clinical practice.
Based on individual’s MRI scans, brain age model has shown great potentials for serving as imaging markers for monitoring normal ageing (NA), as well as for identifying the ones in the pre-diagnostic phase of age-related neurodegenerative diseases. In this study, we investigated the brain age models in normal ageing and mild cognitive impairments (MCI) converters and their values in classifying MCI conversion. Pre-trained brain age model was constructed using the quantitative measures of morphometry included total intracranial volume (TIV), gray matter volume (GMV) and cortical thickness. With comparable chronological age, MCI converters showed significant increased brain age than NA adults at all time points. Higher brain age were associated with worse global cognition. This is the first demonstration that MRI-informed brain age models exhibit feature-specific patterns. The greater GMV-based brain age observed in MCI converters may provide new evidence for identifying the individuals at the early stage of neurodegeneration. Our findings added value to existing quantitative imaging markers and might help to improve disease monitoring and accelerate personalized treatments in clinical practice.
摘要：
背景：大脑年龄模型，包括估计的大脑年龄和大脑预测的年龄差异（brain-PAD），已经显示出作为监测正常衰老的成像标记的巨大潜力，以及用于识别处于神经退行性疾病诊断前阶段的个体。
目的：本研究旨在探讨正常老化和轻度认知障碍（MCI）转化者的脑年龄模型及其在MCI转化分类中的价值。
方法：使用剑桥老龄化和神经科学中心（Cam-CAN）项目（N=609）的结构磁共振成像（MRI）数据构建预训练脑年龄模型。使用基线建立被测大脑年龄模型，来自正常年龄（NA）成年人（n=32）和MCI转换器（n=22）的1年和3年随访MRI数据来自开放获取成像研究系列（OASIS-2）。形态计量学的定量测量包括颅内总体积（TIV），灰质体积（GMV）和皮质厚度。使用支持向量机（SVM）算法根据个体的形态特征计算脑年龄模型。
结果：具有可比的实际年龄，MCI转换器显示出基于TIV的显着增加（基线：P=0.021；1年随访：P=0.037；3年随访：P=0.001），并且在所有时间点基于GMV的大脑年龄均高于NA成年人。较高的脑PAD评分与较差的整体认知相关。发现基于TIV（AUC=0.698）和基于左GMV的脑年龄（AUC=0.703）的可接受分类性能，这可以在基线上区分MCI转换器和NA成年人。
结论：这是首次证明MRI告知的大脑年龄模型表现出特定特征模式。在MCI转换器中观察到的更大的基于GMV的脑年龄可能为识别神经变性早期阶段的个体提供新的证据。我们的发现增加了现有定量成像标记的价值，并可能有助于改善疾病监测并加速临床实践中的个性化治疗。
根据个人的MRI扫描,脑年龄模型显示出作为监测正常衰老(NA)的成像标记的巨大潜力，以及用于识别与年龄相关的神经退行性疾病的诊断前阶段。在这项研究中,我们调查了正常衰老和轻度认知障碍(MCI)转化者的脑年龄模型及其在MCI转化分类中的价值.使用形态计量学的定量测量构建预训练的脑年龄模型，包括颅内总体积（TIV），灰质体积（GMV）和皮质厚度。在相当的实际年龄下，MCI转化者在所有时间点都显示出比NA成人显著增加的脑年龄。较高的大脑年龄与较差的整体认知有关。这是MRI告知的大脑年龄模型表现出特定特征模式的第一个证明。在MCI转换器中观察到的更大的基于GMV的脑年龄可能为识别神经变性早期阶段的个体提供新的证据。我们的发现增加了现有定量成像标记的价值，并可能有助于改善疾病监测并加速临床实践中的个性化治疗。
目的：本研究旨在探讨正常老化和轻度认知障碍（MCI）转化者的脑年龄模型及其在MCI转化分类中的价值。
方法：使用剑桥老龄化和神经科学中心（Cam-CAN）项目（N=609）的结构磁共振成像（MRI）数据构建预训练脑年龄模型。使用基线建立被测大脑年龄模型，来自正常年龄（NA）成年人（n=32）和MCI转换器（n=22）的1年和3年随访MRI数据来自开放获取成像研究系列（OASIS-2）。形态计量学的定量测量包括颅内总体积（TIV），灰质体积（GMV）和皮质厚度。使用支持向量机（SVM）算法根据个体的形态特征计算脑年龄模型。
结果：具有可比的实际年龄，MCI转换器显示出基于TIV的显着增加（基线：P=0.021；1年随访：P=0.037；3年随访：P=0.001），并且在所有时间点基于GMV的大脑年龄均高于NA成年人。较高的脑PAD评分与较差的整体认知相关。发现基于TIV（AUC=0.698）和基于左GMV的脑年龄（AUC=0.703）的可接受分类性能，这可以在基线上区分MCI转换器和NA成年人。
结论：这是首次证明MRI告知的大脑年龄模型表现出特定特征模式。在MCI转换器中观察到的更大的基于GMV的脑年龄可能为识别神经变性早期阶段的个体提供新的证据。我们的发现增加了现有定量成像标记的价值，并可能有助于改善疾病监测并加速临床实践中的个性化治疗。
根据个人的MRI扫描,脑年龄模型显示出作为监测正常衰老(NA)的成像标记的巨大潜力，以及用于识别与年龄相关的神经退行性疾病的诊断前阶段。在这项研究中,我们调查了正常衰老和轻度认知障碍(MCI)转化者的脑年龄模型及其在MCI转化分类中的价值.使用形态计量学的定量测量构建预训练的脑年龄模型，包括颅内总体积（TIV），灰质体积（GMV）和皮质厚度。在相当的实际年龄下，MCI转化者在所有时间点都显示出比NA成人显著增加的脑年龄。较高的大脑年龄与较差的整体认知有关。这是MRI告知的大脑年龄模型表现出特定特征模式的第一个证明。在MCI转换器中观察到的更大的基于GMV的脑年龄可能为识别神经变性早期阶段的个体提供新的证据。我们的发现增加了现有定量成像标记的价值，并可能有助于改善疾病监测并加速临床实践中的个性化治疗。
