Decentralized temporal independent component analysis: Leveraging fMRI data in collaborative settings

[1]  Anand D. Sarwate,et al.  Differentially Private Distributed Principal Component Analysis , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Stephen M. Smith,et al.  Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data , 2017, NeuroImage.

[3]  Vince D. Calhoun,et al.  Sparsity and Independence: Balancing Two Objectives in Optimization for Source Separation with Application to fMRI Analysis , 2017, J. Frankl. Inst..

[4]  Krzysztof J. Gorgolewski,et al.  OpenNeuro – a free online platform for sharing and analysis of neuroimaging data , 2017 .

[5]  Vince D. Calhoun,et al.  Cooperative learning: Decentralized data neural network , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[6]  Anand D. Sarwate,et al.  Decentralized independent vector analysis , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  Giuseppe Ateniese,et al.  Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning , 2017, CCS.

[8]  Anders M. Dale,et al.  ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide , 2017, NeuroImage.

[9]  Anand D. Sarwate,et al.  COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data , 2016, Front. Neurosci..

[10]  Vince D. Calhoun,et al.  Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling , 2016, IEEE Journal of Selected Topics in Signal Processing.

[11]  Anand D. Sarwate,et al.  Privacy-preserving source separation for distributed data using independent component analysis , 2016, 2016 Annual Conference on Information Science and Systems (CISS).

[12]  Vince D. Calhoun,et al.  Memory Efficient PCA Methods for Large Group ICA , 2016, Front. Neurosci..

[13]  I. Melle,et al.  Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium , 2016, Molecular Psychiatry.

[14]  Anand D. Sarwate,et al.  Large scale collaboration with autonomy: Decentralized data ICA , 2015, 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP).

[15]  Rogers F. Silva,et al.  Comparison of PCA approaches for very large group ICA , 2015, NeuroImage.

[16]  Thomas E. Nichols,et al.  Common genetic variants influence human subcortical brain structures , 2015, Nature.

[17]  Krzysztof J. Gorgolewski,et al.  Making big data open: data sharing in neuroimaging , 2014, Nature Neuroscience.

[18]  Martin A. Lindquist,et al.  Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach , 2014, NeuroImage.

[19]  Thomas E. Nichols,et al.  The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data , 2014, Brain Imaging and Behavior.

[20]  Jianfeng Feng,et al.  Attention-Dependent Modulation of Cortical Taste Circuits Revealed by Granger Causality with Signal-Dependent Noise , 2013, PLoS Comput. Biol..

[21]  Barak A. Pearlmutter,et al.  Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence , 2013, PLoS ONE.

[22]  Oluwasanmi Koyejo,et al.  Toward open sharing of task-based fMRI data: the OpenfMRI project , 2013, Front. Neuroinform..

[23]  Roland N. Boubela,et al.  Beyond Noise: Using Temporal ICA to Extract Meaningful Information from High-Frequency fMRI Signal Fluctuations during Rest , 2013, Front. Hum. Neurosci..

[24]  V. Calhoun,et al.  Multisubject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery , 2012, IEEE Reviews in Biomedical Engineering.

[25]  Vince D. Calhoun,et al.  SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability , 2012, NeuroImage.

[26]  Stephen M. Smith,et al.  Temporally-independent functional modes of spontaneous brain activity , 2012, Proceedings of the National Academy of Sciences.

[27]  Tohru Ozaki,et al.  Time Series Modeling of Neuroscience Data , 2012 .

[28]  Tao Zhang,et al.  Comparison between spatial and temporal independent component analysis for blind source separation in fMRI data , 2011, 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI).

[29]  Vince D. Calhoun,et al.  EEGIFT: Group Independent Component Analysis for Event-Related EEG Data , 2011, Comput. Intell. Neurosci..

[30]  Rex E. Jung,et al.  A Baseline for the Multivariate Comparison of Resting-State Networks , 2011, Front. Syst. Neurosci..

[31]  Alexander J. Smola,et al.  Parallelized Stochastic Gradient Descent , 2010, NIPS.

[32]  Aapo Hyvärinen,et al.  Source Separation and Higher-Order Causal Analysis of MEG and EEG , 2010, UAI.

[33]  Tülay Adali,et al.  Complex Independent Component Analysis by Entropy Bound Minimization , 2010, IEEE Transactions on Circuits and Systems I: Regular Papers.

[34]  Harold W. Kuhn,et al.  The Hungarian method for the assignment problem , 1955, 50 Years of Integer Programming.

[35]  Gideon S. Mann,et al.  Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models , 2009, NIPS.

[36]  Fabrizio Esposito,et al.  Neural network of speech monitoring overlaps with overt speech production and comprehension networks: A sequential spatial and temporal ICA study , 2009, NeuroImage.

[37]  I Daubechies,et al.  Independent component analysis for brain fMRI does not select for independence , 2009 .

[38]  Vince D. Calhoun,et al.  An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques , 2009, NeuroImage.

[39]  Vince D. Calhoun,et al.  A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data , 2009, NeuroImage.

[40]  Seungjin Choi,et al.  Independent Component Analysis , 2009, Handbook of Natural Computing.

[41]  Nick C Fox,et al.  The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods , 2008, Journal of magnetic resonance imaging : JMRI.

[42]  Mark J. Lowe,et al.  Isolating physiologic noise sources with independently determined spatial measures , 2007, NeuroImage.

[43]  Radu V. Balan,et al.  Estimator for Number of Sources Using Minimum Description Length Criterion for Blind Sparse Source Mixtures , 2007, ICA.

[44]  Vince D. Calhoun,et al.  Performance of blind source separation algorithms for fMRI analysis using a group ICA method. , 2007, Magnetic resonance imaging.

[45]  Vince D. Calhoun,et al.  PARALLEL INDEPENDENT COMPONENT ANALYSIS FOR MULTIMODAL ANALYSIS: APPLICATION TO FMRI AND EEG DATA , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[46]  T. Adali,et al.  Unmixing fMRI with independent component analysis , 2006, IEEE Engineering in Medicine and Biology Magazine.

[47]  Cynthia Dwork,et al.  Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.

[48]  J. Pekar,et al.  Method for multimodal analysis of independent source differences in schizophrenia: Combining gray matter structural and auditory oddball functional data , 2006, Human brain mapping.

[49]  Franklin T. Luk,et al.  Principal Component Analysis for Distributed Data Sets with Updating , 2005, APPT.

[50]  L. K. Hansen,et al.  Independent component analysis of functional MRI: what is signal and what is noise? , 2003, Current Opinion in Neurobiology.

[51]  John G. Neuhoff,et al.  Spatiotemporal Pattern of Neural Processing in the Human Auditory Cortex , 2002, Science.

[52]  Markus Svensén,et al.  ICA of fMRI Group Study Data , 2002, NeuroImage.

[53]  J. Pekar,et al.  fMRI Activation in a Visual-Perception Task: Network of Areas Detected Using the General Linear Model and Independent Components Analysis , 2001, NeuroImage.

[54]  J. Pekar,et al.  A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.

[55]  V D Calhoun,et al.  Spatial and temporal independent component analysis of functional MRI data containing a pair of task‐related waveforms , 2001, Human brain mapping.

[56]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[57]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[58]  B. Biswal,et al.  Blind source separation of multiple signal sources of fMRI data sets using independent component analysis. , 1999, Journal of computer assisted tomography.

[59]  Karl J. Friston.  Modes or models: a critique on independent component analysis for fMRI , 1998, Trends in Cognitive Sciences.

[60]  Andrzej Cichocki,et al.  Stability Analysis of Learning Algorithms for Blind Source Separation , 1997, Neural Networks.

[61]  A. Hyvarinen.  A family of fixed-point algorithms for independent component analysis , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[62]  Aapo Hyvärinen,et al.  A family of fixed-point algorithms for independent component analysis , 1997, ICASSP.

[63]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[64]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[65]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[66]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .