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Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

文献类型：期刊论文

作者Meng, Xing1,2; Jiang, Rongtao1,2; Lin, Dongdong3,4; Bustillo, Juan5; Jones, Thomas5; Chen, Jiayu3,4; Yu, Qingbao3,4; Du, Yuhui3,4; Zhang, Yu1,2; Jiang, Tianzi1,2,7
刊名NEUROIMAGE
出版日期2017-01-15
卷号145页码:218-229
关键词Individualized Prediction Multimodal Matrics Consensus Cognitive Battery (Mccb) Schizophrenia Mri Neuromarker
DOI10.1016/j.neuroimage.2016.05.026
文献子类Article
英文摘要Neuroimaging techniques have greatly enhanced the understanding of neurodiversity (human brain variation across individuals) in both health and disease. The ultimate goal of using brain imaging biomarkers is to perform individualized predictions. Here we proposed a generalized framework that can predict explicit values of the targeted measures by taking advantage of joint information from multiple modalities. This framework also enables whole brain voxel-wise searching by combining multivariate techniques such as ReliefF, clustering, correlation-based feature selection and multiple regression models, which is more flexible and can achieve better prediction performance than alternative atlas-based methods. For 50 healthy controls and 47 schizophrenia patients, three kinds of features derived from resting-state fMRI (fALFF), sMRI (gray matter) and DTI (fractional anisotropy) were extracted and fed into a regression model, achieving high prediction for both cognitive scores (MCCB composite r = 0.7033, MCCB social cognition r = 0.7084) and symptomatic scores (positive and negative syndrome scale [PANSS] positive r = 0.7785, PANSS negative r = 0.7804). Moreover, the brain areas likely responsible for cognitive deficits of schizophrenia, including middle temporal gyrus, dorsolateral prefrontal cortex, striatum, cuneus and cerebellum, were located with different weights, as well as regions predicting PANSS symptoms, including thalamus, striatum and inferior parietal lobule, pinpointing the potential neuromarkers. Finally, compared to a single modality, multimodal combination achieves higher prediction accuracy and enables individualized prediction on multiple clinical measures. There is more work to be done, but the current results highlight the potential utility of multimodal brain imaging biomarkers to eventually inform clinical decision-making. (C) 2016 Elsevier Inc. All rights reserved.
WOS关键词MILD COGNITIVE IMPAIRMENT ; VOXEL-BASED MORPHOMETRY ; BRAIN IMAGING DATA ; WORKING-MEMORY ; ANTIPSYCHOTIC TREATMENT ; DISCRIMINATING SCHIZOPHRENIA ; 1ST-EPISODE SCHIZOPHRENIA ; FUNCTIONAL CONNECTIVITY ; ONSET SCHIZOPHRENIA ; UNCINATE FASCICULUS
WOS研究方向Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000390976200007
资助机构National High-Tech Development Plan (863)(2015AA020513) ; "100 Talents Plan" of the Chinese Academy of Sciences ; Chinese National Science Foundation(81471367) ; Strategic Priority Research Program of the Chinese Academy of Sciences(XDB02060005) ; National Institutes of Health(R01MH084898 ; R01EB006841 ; P20GM103472)
源URL[http://ir.ia.ac.cn/handle/173211/13385]  
专题自动化研究所_脑网络组研究中心
作者单位1.Chinese Acad Sci, Brainnetome Ctr, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Mind Res Network, Albuquerque, NM 87106 USA
4.Lovelace Biomed & Environm Res Inst, Albuquerque, NM 87106 USA
5.Univ New Mexico, Dept Psychiat & Neurosci, Albuquerque, NM 87131 USA
6.Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA
7.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci, Beijing 100190, Peoples R China
8.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Meng, Xing,Jiang, Rongtao,Lin, Dongdong,et al. Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data[J]. NEUROIMAGE,2017,145:218-229.
APA Meng, Xing.,Jiang, Rongtao.,Lin, Dongdong.,Bustillo, Juan.,Jones, Thomas.,...&Calhoun, Vince D..(2017).Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data.NEUROIMAGE,145,218-229.
MLA Meng, Xing,et al."Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data".NEUROIMAGE 145(2017):218-229.

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来源：自动化研究所

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