Predicting brain age with complex networks: From adolescence to adulthood

被引:47
作者
Bellantuono, Loredana [3 ]
Marzano, Luca [3 ]
La Rocca, Marianna [4 ]
Duncan, Dominique [4 ]
Lombardi, Angela [2 ]
Maggipinto, Tommaso [3 ]
Monaco, Alfonso [2 ]
Tangaro, Sabina [2 ,5 ]
Amoroso, Nicola [1 ,2 ]
Bellotti, Roberto [2 ,3 ]
机构
[1] Univ Bari Aldo Moro, Dipartimento Farm Sci Farmaco, Bari, Italy
[2] Ist Nazl Fis Nucl, Sez Bari, Bari, Italy
[3] Univ Bari Aldo Moro, Dipartimento Interateneo Fis, Bari, Italy
[4] Univ Southern Calif, Lab NeuroImaging, USC Stevens Neuroimaging & Informat Inst, Keck Sch Med, Los Angeles, CA 90007 USA
[5] Univ Bari Aldo Moro, Dipartimento Sci Suolo Pianta & Alimenti, Bari, Italy
关键词
Age prediction; Brain; Deep learning; MRI; Complex networks; ABIDE; Centrality measures; DEEP NEURAL-NETWORKS; CENTRALITY; THICKNESS;
D O I
10.1016/j.neuroimage.2020.117458
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7 - 64 years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r = 0.89 and Mean Absolute Error MAE = 2.19 years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE = 2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging.
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页数:9
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