|
|
|
| Classification method of fMRI data based on broad learning system |
Jia-cheng LIU( ),Jun-zhong JI*() |
| Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China |
|
|
|
Abstract A functional magnetic resonance imaging (fMRI) data classification method based on broad learning system was proposed. The deep features of fMRI data were extracted through a simple structure to speed up the classification. Using the time series of the mean values of the voxel in the region of interest in fMRI the input data was constructed. The shallow and deep features of fMRI data were extracted respectively, mapped to feature nodes and enhancement nodes for broad learning, and a model framework was built. Ridge regression was used to inversely calculate the connection weights of the classification model to achieve fMRI data classification. ABIDE I, ABIDE II and ADHD-200 were used to compare the proposed method with six classification methods. Results show that the proposed method can maintain good classification accuracy while reduce training time greatly.
|
|
Received: 04 March 2020
Published: 05 July 2021
|
|
|
| Fund: 国家自然科学基金资助项目（61672065，61906010）；北京市教委科技计划一般项目（KM202010005032） |
|
Corresponding Authors:
Jun-zhong JI
E-mail: from_soldier@sina.com;jjz01@bjut.edu.cn
|
基于宽度学习系统的fMRI数据分类方法
提出基于宽度学习系统的功能性磁共振成像（fMRI）数据分类方法，通过简单结构提取fMRI数据的深层特征，加快分类速度. 使用fMRI中感兴趣区域体素均值的时间序列构造输入数据，分别提取fMRI数据的浅层和深层特征，映射为宽度学习的特征节点和增强节点并构建模型框架，利用岭回归逆计算分类模型的连接权值，实现对fMRI数据的分类. 使用ABIDE Ⅰ、ABIDE Ⅱ和ADHD-200数据集，将所提方法与6种分类方法进行对比实验，结果表明，所提方法可以在保持良好的分类准确率的同时，大幅度降低训练时间.
关键词：
功能性磁共振成像（fMRI）数据分类,
深度学习,
宽度学习系统,
随机特征映射,
特征增强,
岭回归逆
|
|
| [1] |
BELLIVEAU J W, KENNEDY D N, MCKINSTRY R C, et al Functional mapping of the human visual cortex by magnetic resonance imaging[J]. Science, 1991, 254 (5032): 716- 719
doi: 10.1126/science.1948051
|
|
|
| [2] |
COX D D, SAVOY R L Functional magnetic resonance imaging (fMRI)“brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex[J]. Neuro Image, 2003, 19 (2): 261- 270
|
|
|
| [3] |
CHENG B, LIU M, SHEN D, et al Multi-domain transfer learning for early diagnosis of Alzheimer ’ s disease[J]. Brain, 2012, 135 (5): 1498- 1507
doi: 10.1093/brain/aws059
|
|
|
| [4] |
ROSA M J, PORTUGAL L, HAHN T, et al Sparse network-based models for patient classification using fMRI[J]. Neuroimage, 2015, 105: 493- 506
doi: 10.1016/j.neuroimage.2014.11.021
|
|
|
| [5] |
SACCHET M D, PRASAD G, FOLAND-ROSS L C, et al Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory[J]. Frontiers in Psychiatry, 2015, 6: 21
|
|
|
| [6] |
KHAZAEE A, EBRAHIMZADEH A, BABAJANI-FEREMI A Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer ’ s disease[J]. Brain Imaging and Behavior, 2016, 10 (3): 799- 817
doi: 10.1007/s11682-015-9448-7
|
|
|
| [7] |
KHAZAEE A, EBRAHIMZADEH A, BABAJANI-FEREMI A. Automatic classification of Alzheimer's disease with resting-state fMRI and graph theory [C]// 2014 21th Iranian Conference on Biomedical Engineering (ICBME). Tehran: IEEE, 2014: 252-257.
|
|
|
| [8] |
CHENG B, LIU M, SHEN D, et al Multi-domain transfer learning for early diagnosis of Alzheimer ’ s disease[J]. Neuroinformatics, 2017, 15 (2): 115- 132
doi: 10.1007/s12021-016-9318-5
|
|
|
| [9] |
LI H, XUE Z, ELLMORE T M, et al. Identification of faulty DTI-based sub-networks in autism using network regularized SVM [C]// 2012 9th IEEE International Symposium on Biomedical Imaging(ISBI). Barcelona: IEEE, 2012: 550-553.
|
|
|
| [10] |
DODERO L, MINH H Q, SAN BIAGIO M, et al. Kernel-based classification for brain connectivity graphs on the Riemannian manifold of positive definite matrices [C]// 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), Brooklyn: IEEE, 2015: 42-45.
|
|
|
| [11] |
WEE C Y, YAP P T, SHEN D Diagnosis of autism spectrum disorders using temporally distinct resting-state functional connectivity networks[J]. CNS Neuroscience and Therapeutics, 2016, 22 (3): 212- 219
doi: 10.1111/cns.12499
|
|
|
| [12] |
ANDERSON A, DOUGLAS P K, KERR W T, et al Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD[J]. Neuroimage, 2014, 102: 207- 219
doi: 10.1016/j.neuroimage.2013.12.015
|
|
|
| [13] |
MITRA J, SHEN K, GHOSE S, et al Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks[J]. Neuroimage, 2016, 129: 247- 259
doi: 10.1016/j.neuroimage.2016.01.056
|
|
|
| [14] |
RICHIARDI J, GSCHWIND M, SIMIONI S, et al Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity[J]. Neuroimage, 2012, 62 (3): 2021- 2033
doi: 10.1016/j.neuroimage.2012.05.078
|
|
|
| [15] |
ARBABSHIRANI M R, KIEHL K, PEARLSON G, et al Classification of schizophrenia patients based on resting-state functional network connectivity[J]. Frontiers in Neuroscience, 2013, 7: 133
|
|
|
| [16] |
KENDALL A, GAL Y, CIPOLLA R. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics [C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7482-7491.
|
|
|
| [17] |
MESZLENYI R J, BUZA K, VIDNYANSZKY Z Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture[J]. Frontiers in Neuroinformatics, 2017, 11: 61
doi: 10.3389/fninf.2017.00061
|
|
|
| [18] |
PARISOT S, KTENA S I, FERRANTE E, et al. Spectral graph convolutions for population-based disease prediction [C]// International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017.[S.l.]: Springer, 2017: 177-185.
|
|
|
| [19] |
PARISOT S, KTENA S I, FERRANTE E, et al Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer ’ s disease[J]. Medical Image Analysis, 2018, 48: 117- 130
doi: 10.1016/j.media.2018.06.001
|
|
|
| [20] |
HEINSFELD A S, FRANCO A R, CRADDOCK R C, et al Identification of autism spectrum disorder using deep learning and the ABIDE dataset[J]. Neuroimage: Clinical, 2018, 17: 16- 23
doi: 10.1016/j.nicl.2017.08.017
|
|
|
| [21] |
CHEN C L P, LIU Z Broad learning system: An effective and efficient incremental learning system without the need for deep architecture[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 29 (1): 10- 24
|
|
|
| [22] |
YU W, ZHAO C Broad convolutional neural network based industrial process fault diagnosis with incremental learning capability[J]. IEEE Transactions on Industrial Electronics, 2019, 67 (6): 5081- 5091
|
|
|
| [23] |
CHRN C L P, YU D, LIU L Automatic leader-follower persistent formation control for autonomous surface vehicles[J]. IEEE Access, 2018, 7: 12146- 12155
|
|
|
| [24] |
WANG J, ZHAO C. Broad learning system based visual fault diagnosis for electrical equipment thermography images [C]// 2018 Chinese Automation Congress (CAC). Xi'an: IEEE, 2018: 1632-1637.
|
|
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
