Semi-supervised multimodal relevance vector regression improves cognitive performance estimation from imaging and biological biomarkers.
Semi-supervised multimodal relevance vector regression improves cognitive performance estimation from imaging and biological biomarkers.
复制标题
半监督多模态相关向量回归改善了成像和生物标志物的认知表现估计
DOI：
10.1007/s12021-013-9180-7
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发表时间：
2013-07
期刊：
影响因子：
3
通讯作者：
Shen, Dinggang
中科院分区：
文献类型：
--
作者：
Cheng, Bo;Zhang, Daoqiang;Chen, Songcan;Kaufer, Daniel I.;Shen, Dinggang
关键词：
准确估计患者的认知评分可以帮助跟踪神经系统疾病的进展。在本文中，我们提出了一种新的半监督多模态相关向量回归（SM-RVR）方法，用于从多模态成像和生物标志物预测神经系统疾病的临床评分，以帮助评估病理阶段和预测疾病的进展，例如，阿尔茨海默病（AD）。与大多数现有方法不同，我们从多模式（成像和生物学）生物标志物（包括MRI，FDG-PET和CSF）预测临床评分。考虑到轻度认知功能障碍（MCI）受试者的临床评分往往不如AD和正常对照（NC）受试者稳定，这是由于MCI的异质性，我们仅使用MCI受试者的多模态数据，而不使用相应的临床评分，来训练半监督模型以增强AD和NC受试者的临床评分估计。我们还开发了一种新的策略，选择最翔实的MCI科目。我们评估了我们的方法的性能与所有三种模式的数据（MRI，FDG-PET和CSF）从阿尔茨海默病神经影像学倡议（ADNI）数据库的202名受试者。实验结果表明，我们的SM-RVR方法实现了均方根误差（RMSE）为1.91和相关系数（CORR）为0.80估计MMSE分数，也RMSE为4.45和CORR为0.78估计ADAS-Cog分数，在AD研究中表现出非常有前途的性能。
Accurate estimation of cognitive scores for patients can help track the progress of neurological diseases. In this paper, we present a novel semi-supervised multimodal relevance vector regression (SM-RVR) method for predicting clinical scores of neurological diseases from multimodal imaging and biological biomarker, to help evaluate pathological stage and predict progression of diseases, e.g., Alzheimer’s diseases (AD). Unlike most existing methods, we predict clinical scores from multimodal (imaging and biological) biomarkers, including MRI, FDG-PET, and CSF. Considering that the clinical scores of mild cognitive impairment (MCI) subjects are often less stable compared to those of AD and normal control (NC) subjects due to the heterogeneity of MCI, we use only the multimodal data of MCI subjects, but no corresponding clinical scores, to train a semi-supervised model for enhancing the estimation of clinical scores for AD and NC subjects. We also develop a new strategy for selecting the most informative MCI subjects. We evaluate the performance of our approach on 202 subjects with all three modalities of data (MRI, FDG-PET and CSF) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our SM-RVR method achieves a root-mean-square error (RMSE) of 1.91 and a correlation coefficient (CORR) of 0.80 for estimating the MMSE scores, and also a RMSE of 4.45 and a CORR of 0.78 for estimating the ADAS-Cog scores, demonstrating very promising performances in AD studies.
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DOI：
10.1007/978-3-642-04271-3_95
发表时间：
2009
期刊：
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
影响因子：
--
作者：
Hinrichs, Chris;Singh, Vikas;Xu, Guofan;Johnson, Sterling
通讯作者：
Johnson, Sterling
影响因子：
8.9
作者：
Ni, Bingbing;Yan, Shuicheng;Kassim, Ashraf A.
通讯作者：
Kassim, Ashraf A.
影响因子：
158.5
作者：
Reiman, EM;Caselli, RJ;Osborne, D
通讯作者：
Osborne, D
影响因子：
0.8
作者：
Chételat, G;Eustache, F;Desgranges, B
通讯作者：
Desgranges, B
影响因子：
4.2
作者：
Diehl, J;Grimmer, T;Kurz, A
通讯作者：
Kurz, A