Machine learning for brain age prediction: Introduction to methods and clinical applications.

Machine learning for brain age prediction: Introduction to methods and clinical applications.
复制标题

DOI:
10.1016/j.ebiom.2021.103600
复制
发表时间:
2021-10
期刊:
影响因子:
11.1
通讯作者:
Mechelli A
Mechelli A
中科院分区:
医学1区
文献类型:
--
作者:
Baecker L;Garcia-Dias R;Vieira S;Scarpazza C;Mechelli A

文献摘要

参考文献

被引文献

相似文献

机器学习的兴起开启了分析结构神经成像数据的新方法,包括大脑年龄预测。在这篇最新的综述中,我们介绍了脑年龄预测的方法和潜在的临床应用。关于大脑年龄的研究通常涉及创建健康人与年龄相关的神经解剖学变化的回归机器学习模型。然后将该模型应用于新受试者,以预测他们的大脑年龄。预测的大脑年龄和实际年龄之间的差异被称为“大脑年龄差距”。该值被认为反映了神经解剖学异常,可能是整体大脑健康的标志。它可能有助于早期发现脑功能障碍,并支持鉴别诊断,预后和治疗选择。这些应用可能会导致对年龄相关疾病的更及时和更有针对性的干预。
The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning model of age-related neuroanatomical changes in healthy people. This model is then applied to new subjects to predict their brain age. The difference between predicted brain age and chronological age in a given individual is known as ‘brain-age gap’. This value is thought to reflect neuroanatomical abnormalities and may be a marker of overall brain health. It may aid early detection of brain-based disorders and support differential diagnosis, prognosis, and treatment choices. These applications could lead to more timely and more targeted interventions in age-related disorders.
DOI: 10.1002/ana.24367
发表时间: 2015-04
影响因子: 11.2
作者:
Cole, James H.;Leech, Robert;Sharp, David J.
通讯作者: Sharp, David J.
DOI: 10.1212/wnl.0000000000012289
发表时间: 2021-08-10
期刊: Neurology
影响因子: 9.9
作者:
de Bézenac CE;Adan G;Weber B;Keller SS
通讯作者: Keller SS
DOI: 10.1016/j.neuroimage.2018.03.075
发表时间: 2018-07-15
期刊: NEUROIMAGE
影响因子: 5.7
作者:
Becker, Benjamin Gutierrez;Klein, Tassilo;Wachinger, Christian
通讯作者: Wachinger, Christian
脑年龄预测:使用基于区域和体素的形态学数据进行机器学习模型的比较。
DOI: 10.1002/hbm.25368
发表时间: 2021-06-01
影响因子: 4.8
作者:
Baecker L;Dafflon J;da Costa PF;Garcia-Dias R;Vieira S;Scarpazza C;Calhoun VD;Sato JR;Mechelli A;Pinaya WHL
通讯作者: Pinaya WHL
DOI: 10.1024/1662-9647/a000074
发表时间: 2012-12-01
影响因子: 1
作者:
Franke, Katja;Gaser, Christian
通讯作者: Gaser, Christian