- 个人中心
- 退出登录
浏览全部资源
扫码关注微信
Method of functional brain network modeling with group similarity constraint for mild cognitive impairment classification
- Chinese Journal of Intelligent Science and Technology Vol. 1, Issue 2, Pages: 145-153(2019)
- 作者机构：
1. 南京航空航天大学计算机科学与技术学院，江苏 南京 210016
2. 上海全景医学影像诊断中心，上海 200233
3. 山东莒县妇幼保健医院，山东 日照 276500
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.201922
CLC： TP311.134.3-
Published Online：2019-06，
Published：20 June 2019
- 稿件说明：
移动端阅览
Weikai LI, Xin GAO, Tongjian JI, et al. Method of functional brain network modeling with group similarity constraint for mild cognitive impairment classification[J]. Chinese journal of intelligent science and technology, 2019, 1(2): 145-153.
Weikai LI, Xin GAO, Tongjian JI, et al. Method of functional brain network modeling with group similarity constraint for mild cognitive impairment classification[J]. Chinese journal of intelligent science and technology, 2019, 1(2): 145-153. DOI： 10.11959/j.issn.2096-6652.201922.
摘要
功能脑网络为理解大脑功能激活模式及大脑信息传递结构提供了一种有效的生物标记，如何更加有效地利用先验信息构建准确的脑网络模型尤为重要。提出了一种基于群体相似性约束的功能脑网络模型，通过引入张量低秩约束，利用张量的核范数，求解一个在组内具有低秩先验的脑网络组。这种方法利用组内的相似性约束，收缩脑网络的解空间，从而有效地构建更优的脑网络模型。将所构建的脑网络用于轻度认知障碍的判别任务，实验结果表明，所提出的基于群体相似性约束的脑网络建模方法，能够构建出更具判别性的脑网络，并得到了与以往研究一致的显著性连边，进一步验证了所提出方法的有效性。
Abstract
Functional brain network provides an effective biomarker for understanding brain activation patterns
the development of neurodegenerative diseases
and the structure of brain signaling.How to use priori information to build accurate brain network is particularly important in subsequent applications.A functional brain network construction model based on group similarity constraints was proposed.By introducing a tensor low rank constraint and using the nuclear norm of the tensor
a brain network group with a low rank prior in the group was solved.The group similarity constraints to shrink the solution space of the brain network was used
thus constructing a better functional brain network effectively.For evaluating the performance of the proposed method
the constructed brain network was adopted for the discriminative task of mild cognitive impairment.The experimental results show that the proposed functional brain network modeling method based on group similarity constraints can construct more discriminative brain network.In addition
based on the brain network constructed by the proposed method
the significant connections consistent with previous studies are obtained
and the effectiveness of the proposed method is further verified.
关键词
Keywords
references
GAUGLER J , JAMES B , JOHNSON T , et al . 2016 Alzheimer’s disease facts and figures [J ] . Alzheimers ＆ Dementia , 2016 , 12 ( 4 ): 459 - 509 .
BAIN L J , JEDRZIEWSKIA K ， MORRISON-BOGORAD M , et al . Healthy brain aging:a meeting report from the Sylvan M.Cohen annual retreat of the University of Pennsylvania Institute on Aging [J ] . Alzheimers ＆ Dementia , 2008 , 4 ( 6 ): 443 - 446 .
WEE C Y,YAPP-T , ZHANG D Q , et al . Identification of MCI individuals using structural and functional connectivity networks [J ] . NeuroImage , 2012 , 59 ( 3 ): 2045 - 2056 .
GRUNDMAN M , PETERSEN R C , FERRIS S H , et al . Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials [J ] . Archives of Neurology , 2004 , 61 ( 1 ): 5 - 9 .
MISRA C , FAN Y , DAVATZIKOS C . Baseline and longitudinal patterns of brain atrophy in MCI patients,and their use in prediction of short-term conversion to AD:results from ADNI [J ] . Alzheimers ＆Dementia , 2009 , 44 ( 4 ): 1415 - 1422 .
Alzheimer’s Association . 2017 Alzheimer’s disease facts and figures [R ] . 2017 .
BRUNETTI M , BELARDINELLI P , GRATTA C D , et al . Human brain activation elicited by the localization of sounds delivering at attended or unattended positions:an fMRI/MEG study [J ] . Cognitive Processing , 2006 , 7 ( 1 ): 116 - 117 .
LEE J H , DURAND R , GRADINARU V , et al . Global and local fMRI signals driven by neurons defined optogenetically by type and wiring [J ] . Nature , 2010 , 465 ( 7299 ): 788 - 792 .
KEVIN W , DOUG W , MATTHIAS S , et al . Correspondence of visual evoked potentials with FMRI signals in human visual cortex [J ] . Brain Topography , 2008 , 21 ( 2 ): 86 - 92 .
ROSAM J , PORTUGAL L ， HAHNT , et al . Sparse network-based models for patient classification using fMRI [J ] . NeuroImage , 2015 , 105 ( 3 ): 493 - 506 .
STAM C J . Modern network science of neurological disorders [J ] . Nature Reviews Neuroscience , 2014 , 15 ( 10 ): 683 - 695 .
WEE C Y , YAP P T , ZHANG D Q , et al . Identification of MCI individuals using structural and functional connectivity networks [J ] . NeuroImage , 2012 , 59 ( 3 ): 2045 - 2056 .
SPORNS O . Networks of the brain [J ] . General , 2011 .
SMITH S M , MILLER K L , SALIMIK G , et al . Network modelling methods for fMRI [J ] . NeuroImage , 2011 , 54 ( 2 ): 875 - 891 .
GOTTS S J , SIMMONS W K , MILBURY L A , et al . Fractionation of social brain circuits in autism spectrum disorders [J ] . Brain:A Journal of Neurology , 2012 , 135 ( 9 ): 2711 - 2725 .
THEIJE C G M D , WU J B , SILVA S L D , et al . Pathways underlying the gut-to-brain connection in autism spectrum disorders as future targets for disease management [J ] . European Journal of Pharmacology , 2011 ,S1( 9 ): 70 - 80 .
FAN Y , BROWNDYKE J N . MCI diagnosis via manifold based classification of functional brain networks [J ] . Alzheimers ＆ Dementia , 2010 , 6 ( 6 ):16.
WEE C Y , YAP P T , ZHANG D Q , et al . Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification [J ] . Brain Structure ＆ Function , 2014 , 219 ( 2 ): 641 - 656 .
YU R , ZHANG H , AN L , et al . Correlation-weighted sparse group representation for brain network construction in MCI classification [M ] . Springer International Publishing , 2016 .
LIU F , WEE C Y , CHEN H , et al . Inter-modality relationship constrained multi-task feature selection for AD/MCI classification [C ] // Miccai International Conference on Medical Image Computing ＆Computer-assisted Intervention , 2012 : 308 - 315 .
SUPEKAR K , MENON V , RUBIN D , et al . Network analysis of intrinsic functional brain connectivity in Alzheimer's disease [J ] . PloS Computational Biology , 2008 , 4 ( 6 ): 1 - 11 .
HUANG S , LI J , SUN L , et al . Learning brain connectivity of Alzheimer's disease from neuroimaging data [C ] // Conference on Neural Information Processing Systems . 2009 : 808 - 816 .
LI W K , QIAO L S , ZHANG L M , et al . Functional brain network estimation with time series self-scrubbing [J ] . IEEE Journal of Biomedical and Health Informatics , 2019 , 99 :1.
SMITH S M , MILLER K L , SALIMI K G , et al . Network modelling methods for FMRI [J ] . NeuroImage , 2011 , 54 ( 2 ): 875 - 891 .
LI W , WANG Z , ZHANG L , et al . Remodeling Pearson's correlation for functional brain network estimation and autism spectrum disorder identification [J ] . Frontiers in Neuroinformatics , 2017 , 11 :55.
LEE H , LEE D S , KANG H , et al . Sparse brain network recovery under compressed sensing [J ] . IEEE Transactions on Medical Imaging , 2011 , 30 ( 5 ): 1154 - 1165 .
ZHOU L , WANG L , OGUNBONA P . Discriminative sparse inverse covariance matrix:application in brain functional network classification [C ] // IEEE Conference on Computer Vision and Pattern Recognition , 2014 : 3097 - 3104 .
SPORNS O , TONONI G,KÖTTER R . The human connectome:a structural description of the human brain [J ] . Plos Computational Biology , 2005 , 1 ( 4 ):42.
QIAO L , ZHANG H , KIM M , et al . Estimating functional brain networks by incorporating a modularity prior [J ] . NeuroImage , 2016 ,( 141 ): 399 - 407 .
WEE C Y , YAP P T , SHEN D . Diagnosis of autism spectrum disorders using temporally distinct resting-state functional connectivity networks [J ] . CNS Neuroscience ＆ Therapeutics , 2016 , 22 ( 3 ).
LI W , ZHANG L , QIAO L , et al . Towards a better estimation of functional brain network for mild cognitive impairment identification:a transfer learning view [J ] . bioRxiv , 2019 : 684 - 779 .
LIU X , BOURENNANE S , FOSSATI C . Denoising of hyperspectral images using the PARAFAC model and statistical performance analysis [J ] . IEEE Transactions on Geoscience ＆ Remote Sensing , 2012 , 50 ( 10 ): 3717 - 3724 .
SPORNS O , TONONI G,KÖTTERR . The human connectome:a structural description of the human brain [J ] . PloS Computational Biology , 2005 , 1 ( 4 ):42.
SMITH S M , VIDAURRE D , BECKMANNC F , et al . Functional connectomics from resting-state fMRI [J ] . Trends in Cognitive Sciences , 2013 , 17 ( 12 ): 666 - 682 .
FRIEDMAN J , HASTIE T , TIBSHIRANI R . Sparse inverse covariance estimation with the graphical lasso [J ] . Biostatistics , 2008 , 9 ( 3 ):432.
HORN A , OSTWALD D , REISERT M , et al . The structural-functional connectome and the default mode network of the human brain [J ] . NeuroImage , 2014 , 102 : 142 - 151 .
FRISTON K J , KAHAN J , BISWAL B , et al . A DCM for resting state fMRI [J ] . NeuroImage , 2013 , 94 ( 100 ):396.
QIAO L , ZHANG H , KIM M , et al . Estimating functional brain networks by incorporating a modularity prior [J ] . NeuroImage , 2016 , 141 :399.
MARDIA K V , KENT J T , BIBBY J M . Multivariate analysis [J ] . Mathematical Gazette , 1979 , 37 ( 1 ): 123 - 131 .
LEE H , DONG S L , KANG H , et al . Sparse brain network recovery under compressed sensing [J ] . IEEE Transactions on Medical Imaging , 2011 , 30 ( 5 ): 1154 - 1165 .
DONOHO D L , ELAD M . Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ [J ] . Proceedings of the National Academy of Sciences , 2003 , 100 ( 5 ): 2197 - 2202 .
MEINSHAUSEN N ， BÜHLMANN P . High-dimensional graphs and variable selection with the Lasso [J ] . Annals of Statistics , 2006 , 34 ( 3 ): 1436 - 1462 .
TOMIOKA R , SUGIYAMA M . Dual augmented Lagrangian method for efficient sparse reconstruction [J ] . IEEE Signal Processing Letters , 2009 , 16 ( 12 ): 1067 - 1070 .
JACK C R , BERNSTEIN M A , FOX N C , et al . The Alzheimer's disease neuroimaging initiative (ADNI):MRI methods [J ] . Journal of Magnetic Resonance Imaging , 2010 , 27 ( 4 ): 685 - 691 .
ZHOU Y Y , QIAO L S , LI W K , et al . Simultaneous estimation of lowand high-order functional connectivity for identifying mild cognitive impairment [J ] . Frontiers in Neuroinformatics , 2018 , 12 :3.
YAN C G , ZANG Y F . DPARSF:a MATLAB toolbox for “pipeline”data analysis of resting-state fMRI [J ] . Frontiers in Systems Neuroscience , 2010 , 4 ( 13 ):13.
CHEN X , ZHANG H , GAO Y , et al . High-order resting-state functional connectivity network for MCI classification [J ] . Human Brain Mapping , 2016 , 37 ( 9 ): 3282 - 3296 .
CHEN X , ZHANG H , ZHANG L , et al . Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification [J ] . Human Brain Mapping , 2017 , 38 ( 10 ):5019.
POWER J D , BARNES K A , SNYDER A Z , et al . Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion [J ] . NeuroImage , 2012 , 5 ( 3 ): 2142 - 2154 .
TZOURIOM N , LANDEAU B , PAPATHANASSIOU D , et al . Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain [J ] . NeuroImage , 2002 , 15 ( 1 ): 273 - 289 .
郑南宁 . 人工智能新时代 [J ] . 智能科学与技术学报 , 2019 , 1 ( 1 ): 1 - 3 .
ZHENG N N . The new era of artificial intelligence [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 1 ): 1 - 3 .
张钹 . 人工智能进入后深度学习时代 [J ] . 智能科学与技术学报 , 2019 , 1 ( 1 ): 4 - 6 .
ZHANG B . Artificial intelligence is entering the post deep-learning era [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 1 ): 4 - 6 .
0
Views
1217
下载量
0
CSCD
- L-PDFDownload
- XMLDownload
- Meta-XMLDownload
- BibTeXDownload
- EndnoteDownload
- RefMan Download
- NoteExpressDownload
- NoteFirstDownload
Publicity Resources
Related Articles
Related Author
Related Institution
- Postal code：100079
- Tel：（010）53879206 Email：cjist@bjxintong.com.cn
- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
- Total Visits：35641 Visits Today：116
