Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study.
Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study.
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多模式脑年龄预测和心血管风险：白厅II MRI子研究。
DOI：
10.1016/j.neuroimage.2020.117292
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发表时间：
2020-11-15
期刊：
影响因子：
5.7
通讯作者：
Ebmeier KP
中科院分区：
文献类型：
--
作者：
de Lange AG;Anatürk M;Suri S;Kaufmann T;Cole JH;Griffanti L;Zsoldos E;Jensen DEA;Filippini N;Singh-Manoux A;Kivimäki M;Westlye LT;Ebmeier KP
心血管危险因素与大脑年龄较大有关。与灰质相比，血压与白质的相关性更强。静息状态功能连接提供较低的大脑年龄预测准确性。大脑年龄预测的准确性取决于样本大小和年龄范围。脑年龄正在成为一种广泛应用的基于成像的神经老化生物标志物，也是大脑完整性和健康的潜在指标。我们使用机器学习和成像衍生的灰质 (GM) 形态、白质微结构 (WM) 和静息态功能连接 (FC) 测量方法，估计了 Whitehall II (WHII) MRI 队列中的多模态和模态特异性脑年龄。结果表明，当模型中包含多种成像模式时，预测精度会提高（R2 = 0.30，95% CI [0.24，0.36]）。特定模态的 GM 和 WM 模型显示出相似的性能（分别为 R2 = 0.22 [0.16, 0.27] 和 R2 = 0.24 [0.18, 0.30]），而 FC 模型显示出最低的预测精度（R2 = 0.002 [-0.005, 0.008]），这表明与结构测量相比，FC 特征与实际年龄的相关性较小。后续分析表明，在英国生物银行的匹配子样本中，FC 预测同样较低，尽管 FC 预测始终低于 GM 预测，但随着样本量和年龄范围的增加，准确性得到提高。 WHII 队列中的心血管危险因素，包括高血压、饮酒和中风风险评分，均与大脑衰老相关。与灰质相比，血压与白质的关联性更强，而酒精摄入量和中风风险与这些方式的关联没有观察到差异。总之，基于机器学习的大脑年龄预测可以降低神经影像数据的维度，从而提供有意义的个体大脑衰老生物标志物。然而，模型性能取决于研究特定特征，包括样本量和年龄范围，这可能会导致研究结果存在差异。
Cardiovascular risk factors are associated with older brain age. Blood pressure is more strongly associated with white matter compared to gray matter. Resting state functional connectivity provides lower brain-age prediction accuracy. Brain-age prediction accuracy depends on sample size and age range. Brain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II (WHII) MRI cohort using machine learning and imaging-derived measures of gray matter (GM) morphology, white matter microstructure (WM), and resting state functional connectivity (FC). The results showed that the prediction accuracy improved when multiple imaging modalities were included in the model (R2 = 0.30, 95% CI [0.24, 0.36]). The modality-specific GM and WM models showed similar performance (R2 = 0.22 [0.16, 0.27] and R2 = 0.24 [0.18, 0.30], respectively), while the FC model showed the lowest prediction accuracy (R2 = 0.002 [-0.005, 0.008]), indicating that the FC features were less related to chronological age compared to structural measures. Follow-up analyses showed that FC predictions were similarly low in a matched sub-sample from UK Biobank, and although FC predictions were consistently lower than GM predictions, the accuracy improved with increasing sample size and age range. Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with brain aging in the WHII cohort. Blood pressure showed a stronger association with white matter compared to gray matter, while no differences in the associations of alcohol intake and stroke risk with these modalities were observed. In conclusion, machine-learning based brain age prediction can reduce the dimensionality of neuroimaging data to provide meaningful biomarkers of individual brain aging. However, model performance depends on study-specific characteristics including sample size and age range, which may cause discrepancies in findings across studies.
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DOI：
10.1136/bmj.c3666
发表时间：
2010-07-26
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Debette S;Markus HS
通讯作者：
Markus HS
影响因子：
5.7
作者：
Blautzik, Janusch;Vetter, Celine;Meindl, Thomas
通讯作者：
Meindl, Thomas
影响因子：
3.7
作者：
Allen, Elena A.;Damaraju, Eswar;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
影响因子：
9.2
作者：
Brown, Timothy T.;Kuperman, Joshua M.;Chung, Yoonho;Erhart, Matthew;McCabe, Connor;Hagler, Donald J., Jr.;Venkatraman, Vijay K.;Akshoomoff, Natacha;Amaral, David G.;Bloss, Cinnamon S.;Casey, B. J.;Chang, Linda;Ernst, Thomas M.;Frazier, Jean A.;Gruen, Jeffrey R.;Kaufmann, Walter E.;Kenet, Tal;Kennedy, David N.;Murray, Sarah S.;Sowell, Elizabeth R.;Jernigan, Terry L.;Dale, Anders M.
通讯作者：
Dale, Anders M.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y