Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture
暂无分享,去创建一个
[1] Mark W. Woolrich,et al. Network modelling methods for FMRI , 2011, NeuroImage.
[2] Mark W. Woolrich,et al. Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of conditions of mild cognitive impairment , 2014, NeuroImage.
[3] Michael Eickenberg,et al. Machine learning for neuroimaging with scikit-learn , 2014, Front. Neuroinform..
[4] O. Andreassen,et al. Disrupted global metastability and static and dynamic brain connectivity across individuals in the Alzheimer’s disease continuum , 2017, Scientific Reports.
[5] Vince D. Calhoun,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 , 2016, NeuroImage.
[6] Jianfeng Feng,et al. Voxel Selection in fMRI Data Analysis Based on Sparse Representation , 2009, IEEE Transactions on Biomedical Engineering.
[7] Aapo Hyvärinen,et al. Group-PCA for very large fMRI datasets , 2014, NeuroImage.
[8] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[9] Vince D. Calhoun,et al. Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks , 2017, NeuroImage.
[10] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[11] Maximilian Reiser,et al. Classifying fMRI-derived resting-state connectivity patterns according to their daily rhythmicity , 2013, NeuroImage.
[12] Anita E. Bandrowski,et al. The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis , 2012, Front. Neuroinform..
[13] Christian Windischberger,et al. Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.
[14] B. Biswal,et al. Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.
[15] He Li,et al. Disrupted functional connectivity related to differential degeneration of the cingulum bundle in mild cognitive impairment patients. , 2015, Current Alzheimer research.
[16] Catie Chang,et al. Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics , 2015, NeuroImage.
[17] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[18] Bing Chen,et al. An open science resource for establishing reliability and reproducibility in functional connectomics , 2014, Scientific Data.
[19] M. Rietschel,et al. Correlated gene expression supports synchronous activity in brain networks , 2015, Science.
[20] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[21] Hui Ding,et al. Querying and mining of time series data: experimental comparison of representations and distance measures , 2008, Proc. VLDB Endow..
[22] Vince D. Calhoun,et al. Deep learning for neuroimaging: a validation study , 2013, Front. Neurosci..
[23] Ghassan Hamarneh,et al. BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment , 2017, NeuroImage.
[24] Tatia M.C. Lee,et al. Resting-state abnormalities in amnestic mild cognitive impairment: a meta-analysis , 2016, Translational Psychiatry.
[25] E. Bullmore,et al. Undirected graphs of frequency-dependent functional connectivity in whole brain networks , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.
[26] A. Mechelli,et al. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications , 2017, Neuroscience & Biobehavioral Reviews.
[27] Ladislav Peska,et al. Classification of fMRI data using dynamic time warping based functional connectivity analysis , 2016, 2016 24th European Signal Processing Conference (EUSIPCO).
[28] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[29] Jing Yang,et al. Voxelwise meta-analysis of gray matter anomalies in Alzheimer's disease and mild cognitive impairment using anatomic likelihood estimation , 2012, Journal of the Neurological Sciences.
[30] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[31] Eswar Damaraju,et al. Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.
[32] Kristóf Marussy,et al. Hubness-Aware Classification, Instance Selection and Feature Construction: Survey and Extensions to Time-Series , 2015, Feature Selection for Data and Pattern Recognition.
[33] Catie Chang,et al. Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.
[34] Nicole Wenderoth,et al. Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example , 2016, Front. Psychiatry.
[35] Jiaxing Zhang,et al. Scale-Invariant Convolutional Neural Networks , 2014, ArXiv.
[36] Razvan Pascanu,et al. On the Number of Linear Regions of Deep Neural Networks , 2014, NIPS.
[37] Valerie Kirsch,et al. Long-term test-retest reliability of resting-state networks in healthy elderly subjects and with amnestic mild cognitive impairment patients. , 2013, Journal of Alzheimer's disease : JAD.
[38] Tom M. Mitchell,et al. Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.
[39] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[41] Kaustubh Supekar,et al. Sparse logistic regression for whole-brain classification of fMRI data , 2010, NeuroImage.
[42] John Scott Bridle,et al. Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition , 1989, NATO Neurocomputing.
[43] Peter Fransson,et al. The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis , 2008, NeuroImage.
[44] Sida I. Wang,et al. Dropout Training as Adaptive Regularization , 2013, NIPS.
[45] S. Chiba,et al. Dynamic programming algorithm optimization for spoken word recognition , 1978 .
[46] Nicolas Le Roux,et al. The Curse of Highly Variable Functions for Local Kernel Machines , 2005, NIPS.
[47] Vinod Menon,et al. Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[48] John Shawe-Taylor,et al. Sparse network-based models for patient classification using fMRI , 2013, NeuroImage.
[49] Bernard Widrow,et al. 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.
[50] Li Wei,et al. Fast time series classification using numerosity reduction , 2006, ICML.
[51] Dimitris Samaras,et al. Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example , 2016, NeuroImage.
[52] Kuncheng Li,et al. Functional Disconnection and Compensation in Mild Cognitive Impairment: Evidence from DLPFC Connectivity Using Resting-State fMRI , 2011, PloS one.
[53] 智晴 長尾,et al. Deep Neural Network を用いた株式売買戦略の構築 , 2016 .
[54] Vince D. Calhoun,et al. Classification of schizophrenia patients based on resting-state functional network connectivity , 2013, Front. Neurosci..
[55] M. Fox,et al. The global signal and observed anticorrelated resting state brain networks. , 2009, Journal of neurophysiology.
[56] Steven Salzberg,et al. On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach , 1997, Data Mining and Knowledge Discovery.
[57] Michele Volpi,et al. Learning rotation invariant convolutional filters for texture classification , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).
[58] Daniel S. Margulies,et al. Predicting brain-age from multimodal imaging data captures cognitive impairment , 2016, NeuroImage.
[59] Petra Hermann,et al. Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping , 2017, Front. Neurosci..
[60] Ladislav Peska,et al. A Model for Classification Based on the Functional Connectivity Pattern Dynamics of the Brain , 2016, 2016 Third European Network Intelligence Conference (ENIC).
[61] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[62] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[63] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..