Brain functional connections classification method based on significant sparse strong correlation
Ming LI1,2,3(),Li-juan DUAN1,2,3,*(),Wen-jian WANG1,2,3,Qing EN4
1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China 2. Beijing Key Laboratory of Trusted Computing, Beijing 100124, China 3. National Engineering Laboratory for Critical Technologies of Information Security Classified Protection, Beijing 100124, China 4. Artificial Intelligence and Machine Learning (AIML) Lab, School of Computer Science, Carlton University, Ottawa K1S 5B6, Canada
A brain functional connectivity classification method based on significant sparse strong correlation was proposed to solve problems that data dimension of brain functional connectivity is too high and many redundant features affect the accuracy of neural network classification. In this method, the salient features sparse module was used to filter and enhance the original features. The sparse strong correlation feature context fusion module was used to aggregate the salient feature information in different receptive fields. The fully connected neural network was used for classification prediction. The results on ABIDE and ADHD-200 datasets showed that the accuracy of brain functional connection classification algorithm was improved by 10.41% and 12.5% respectively compared with the existing brain functional connection classification algorithm. The visualization results of the important features show that the proposed method can accurately locate the brain regions related to disease, which has a certain practical value.
Ming LI,Li-juan DUAN,Wen-jian WANG,Qing EN. Brain functional connections classification method based on significant sparse strong correlation. Journal of ZheJiang University (Engineering Science), 2022, 56(11): 2232-2240.
Tab.1Gender and age distribution of different data
数据集
模型
ACC
SEN
SPE
AUC
ABIDE
ICA-SVM
63.24
59.51
66.55
63.03
PCA-SVM
58.77
54.34
62.5
58.42
CNN-EW
64.99
62.75
66.76
64.75
Full-BiLSTM
69.29
56.65
79.67
68.16
SCNN
70.94
66.93
73.52
70.23
SSSC(ours)
81.35
78.47
87.88
86.18
ADHD200
ICA-SVM
62.23
36.18
81.7
66.02
PCA-SVM
56.08
42.58
65.84
54.21
CNN-EW
68.71
42.67
84.55
63.61
Full-BiLSTM
69.11
59.36
74.52
66.94
SCNN
69.97
65.28
73.63
69.46
SSSC(ours)
82.47
86.81
75.26
81.43
Tab.2Comparison of evaluation index results for measuring classification performance %
ASD vs TC
Model
ACC/%
SEN/%
SPE/%
AUC/%
SSSC-5%
64.87
13.16
85.22
56.57
SSSC-10%
81.35
78.47
87.88
86.18
SSSC-20%
83.42
76.55
85.69
82.12
SSSC-50%
70.15
19.77
96.15
57.97
SSSC-80%
53.57
0
100
50.00
SSSC-all
53.57
0
100
50.00
ADHD vs TC
Model
ACC/%
SEN/%
SPE/%
AUC/%
SSSC-5%
67.76
20.31
93.61
70.16
SSSC-10%
82.47
86.81
75.26
81.43
SSSC-20%
82.55
84.55
73.12
76.12
SSSC-50%
72.11
23.55
89.11
63.22
SSSC-80%
63.15
15.26
92.33
54.22
SSSC-all
61.77
0
100
50.00
Tab.3Comparison of results of ablation experiments under different number of brain functional connections
Fig.4Comparison of classification performance by fusing
Fig.5Primary functional connection strength of important brain functional connections
Fig.6Important brain functional connections obtained in different datasets
[1]
ITAHASHI T, YAMADA T, WATANABE H, et al Altered network topologies and hub organization in adults with autism: a resting-state fMRI study[J]. PloSone, 2014, 9 (4): e94115
doi: 10.1371/journal.pone.0094115
[2]
LIANG X, WANG J Human connectome: structural and functional brain networks[J]. Chinese Science Bulletin, 2010, 55 (16): 1565- 1583
doi: 10.1360/972009-2150
[3]
SPORNS O Structure and function of complex brain networks[J]. Dialogues in Clinical Neuroscience, 2013, 15 (3): 247- 262
doi: 10.31887/DCNS.2013.15.3/osporns
[4]
STAM C Modern network science of neurological disorders[J]. Nature Reviews Neuroscience, 2014, 15 (10): 683
doi: 10.1038/nrn3801
[5]
WEE C, YAP P, ZHANG D, et al Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification[J]. Brain Structure and Function, 2014, 219 (2): 641- 656
doi: 10.1007/s00429-013-0524-8
ZHOU J, LU Z, SUN J, et al. Feafiner: biomarker identification from medical data through feature generalization and selection[C]// Acm Sigkdd. Chicago: ACM, 2013: 1034-1042.
[8]
KHAZAEE A, EBRAHIMZADEH A, BABAJANIFEREMI 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
[9]
CRADDOCK R, HOLTZHEIMER P, HU X, et al Disease state prediction from resting state FMRI[J]. Neuro Image, 2009, 47 (6): S80
[10]
MESZLÉNYI R, BUZA K, VIDNYÁNSZKY 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
[11]
KAWAHARA J, BROWN C, MILLER S, et al Brainnet CNN: convolutional neural networks for brain networks towards predicting neurodevelopment[J]. NeuroImage, 2017, 146: 1038- 1049
doi: 10.1016/j.neuroimage.2016.09.046
[12]
JIE B, LIU M, LIAN C, et al Designing weighted correlation kernels in convolutional neural networks for functional connectivity based brain disease diagnosis[J]. Medical Image Analysis, 2020, 63
[13]
JEON E, KANG E, LEE J, et al. Enriched representation learning in resting-state fMRI for early MCI diagnosis[C]// MICCAI. Lima: Springer, 2020: 397-406.
[14]
KIM J, CALHOUN V, SHIM E, et al Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: evidence from whole-brain resting-state functional connectivity patterns of schizophrenia[J]. Neuroimage, 2016, 124: 127- 146
doi: 10.1016/j.neuroimage.2015.05.018
[15]
LI H, PARIKH N, HE L A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes[J]. Frontiers in Neuroscience, 2018, 12: 491
doi: 10.3389/fnins.2018.00491
[16]
HUANG J, ZHOU L, WANG L, et al. Integrating functional and structural connectivities via diffusion-convolution-bilinear neural network[C]// Miccai. Shenzhen: Springer, 2019: 691-699.
[17]
WANG M, HUANG J, LIU M, et al Modeling dynamic characteristics of brain functional connectivity networks using resting-state functional MRI[J]. Medical Image Analysis, 2021, 71: 102063
doi: 10.1016/j.media.2021.102063
[18]
SUBBARAJU V, SURESH M, SUNDARAM S, et al Identifying differences in brain activities and an accurate detection of autism spectrum disorder using resting state functional-magnetic resonance imaging: a spatial filtering approach[J]. Medical Image Analysis, 2017, 35: 375- 389
doi: 10.1016/j.media.2016.08.003
[19]
BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate [C]// Iclr. San Diego, 2015.
[20]
LUONG M, PHAM H, MANNING C. Effective approaches to attention-based neural machine translation [C]// Emnlp. Lisbon: the association for computational Linguistics, 2015: 1412−1421.
[21]
YIN W, SCHÜTZE H, XIANG B, et al Abcnn: Attention-based convolutional neural network for modeling sentence pairs[J]. Transactions of the Association for Computational Linguistics, 2016, 4: 259- 272
doi: 10.1162/tacl_a_00097
[22]
VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// Advances in Neural Information Processing Systems. Long Beach: NIPS Poundation, 2017: 5998-6008.
[23]
CHEN M, RADFORD A, CHILD R, et al. Generative pretraining from pixels[C]// International Conference on Machine Learning. Virtual Event: PNVR, 2020: 1691-1703.
[24]
DOSOVITSKIYET A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: Transformers for image recognition at scale [C]// Iclr. Virtual Event: Open Review Net, 2021.
[25]
GAO M, TSAI F, LEE C. Learning a phenotypic-attribute attentional brain connectivity embedding for ADHD classification using rs-fMRI[C]// Embc. Montreal: IEEE, 2020: 5472-5475.
[26]
MA J, ZHU X, YANG D, et al. Attention-guided deep graph neural network for longitudinal alzheimer’s disease analysis[C]// Miccai. Lima: Springer, 2020: 387-396.
[27]
CRADDOCK C, BENHAJALI Y, CHU C, et al The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives[J]. Frontiers in Neuro Informatics, 2013, 7: 27
[28]
BELLEC P, CHU C, CHOUINARDDECORTE F, et al The neuro bureau ADHD-200 preprocessed repository[J]. Neuroimage, 2017, 144: 275- 286
doi: 10.1016/j.neuroimage.2016.06.034
[29]
JI J, CHEN Z, YANG C Convolutional neural network with sparse strategies to classify dynamic functional connectivity[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 26 (3): 1219- 1228
[30]
CUI Z, GONG G The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features[J]. Neuroimage, 2018, 178: 622- 637
doi: 10.1016/j.neuroimage.2018.06.001
[31]
XING X, JI J, YAO Y. Convolutional neural network with element-wise filters to extract hierarchical topological features for brain networks[C]// IEEE International Conference on Bioinformatics and Biomedicine. Modrid: IEEE, 2018: 780-783.
[32]
YAN W, ZHANG H, SUI J, et al. Deep chronnectome learning via full bidirectional long short-term memory networks for MCI diagnosis[C]// International Conference on Medical Image Computing and Computer-assisted Intervention. Granada: Springer, Cham, 2018: 249-257.
[33]
HEINSFELD A, FRANCO A, CRADDOCK R, 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
