Identifying and Characterizing Resting State Networks in Temporally Dynamic Functional Connectomes

[1]  Dajiang Zhu,et al.  Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients , 2014, Human brain mapping.

[2]  Eswar Damaraju,et al.  Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.

[3]  Xin Zhang,et al.  Characterization of task-free and task-performance brain states via functional connectome patterns , 2013, Medical Image Anal..

[4]  Cerebral Cortex doi:10.1093/cercor/bhs072 DICCCOL: Dense Individualized and Common Connectivity-Based Cortical Landmarks , 2012 .

[5]  Tuo Zhang,et al.  Inferring Group-Wise Consistent Multimodal Brain Networks via Multi-View Spectral Clustering , 2013, IEEE Transactions on Medical Imaging.

[6]  Xin Zhang,et al.  Characterization of Task-Free/Task-Performance Brain States , 2012, MICCAI.

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

[8]  David Zhang,et al.  Fisher Discrimination Dictionary Learning for sparse representation , 2011, 2011 International Conference on Computer Vision.

[9]  Hal Daumé,et al.  A Co-training Approach for Multi-view Spectral Clustering , 2011, ICML.

[10]  Degang Zhang,et al.  Optimization of functional brain ROIs via maximization of consistency of structural connectivity profiles , 2011, NeuroImage.

[11]  Vince D. Calhoun,et al.  Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients , 2010, NeuroImage.

[12]  Baoxin Li,et al.  Discriminative K-SVD for dictionary learning in face recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Guillermo Sapiro,et al.  Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.

[14]  Catie Chang,et al.  Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.

[15]  Kent A. Kiehl,et al.  A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia , 2010, Magnetic Resonance Materials in Physics, Biology and Medicine.

[16]  Guillermo Sapiro,et al.  Non-local sparse models for image restoration , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[17]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Sham M. Kakade,et al.  Multi-view clustering via canonical correlation analysis , 2009, ICML '09.

[19]  M. Raichle,et al.  Cortical network functional connectivity in the descent to sleep , 2009, Proceedings of the National Academy of Sciences.

[20]  M. P. van den Heuvel,et al.  Normalized Cut Group Clustering of Resting-State fMRI Data , 2008, PloS one.

[21]  D. Schacter,et al.  The Brain's Default Network , 2008, Annals of the New York Academy of Sciences.

[22]  Michael Elad,et al.  Sparse Representation for Color Image Restoration , 2008, IEEE Transactions on Image Processing.

[23]  V. Calhoun,et al.  Selective changes of resting-state networks in individuals at risk for Alzheimer's disease , 2007, Proceedings of the National Academy of Sciences.

[24]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[25]  Abraham Z. Snyder,et al.  A default mode of brain function: A brief history of an evolving idea , 2007, NeuroImage.

[26]  C. Gilbert,et al.  Brain States: Top-Down Influences in Sensory Processing , 2007, Neuron.

[27]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[28]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[29]  Stephen M. Smith,et al.  fMRI resting state networks define distinct modes of long-distance interactions in the human brain , 2006, NeuroImage.

[30]  Silke Dodel,et al.  Detection of signal synchronizations in resting-state fMRI datasets , 2006, NeuroImage.

[31]  P. Matthews,et al.  Blood oxygenation level dependent contrast resting state networks are relevant to functional activity in the neocortical sensorimotor system , 2005, Experimental Brain Research.

[32]  Stephen M. Smith,et al.  Investigations into resting-state connectivity using independent component analysis , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[33]  S. Bickel,et al.  Multi-view clustering , 2004 .

[34]  Michelle Hampson,et al.  Changes in functional connectivity of human MT/V5 with visual motion input , 2004, Neuroreport.

[35]  Tianzi Jiang,et al.  Modulation of functional connectivity during the resting state and the motor task , 2004, Human brain mapping.

[36]  M. Greicius,et al.  Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI , 2004, Proc. Natl. Acad. Sci. USA.

[37]  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.

[38]  Dietmar Cordes,et al.  Hierarchical clustering to measure connectivity in fMRI resting-state data. , 2002, Magnetic resonance imaging.

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

[40]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[41]  V. Haughton,et al.  Mapping functionally related regions of brain with functional connectivity MR imaging. , 2000, AJNR. American journal of neuroradiology.

[42]  S Posse,et al.  Functional magnetic resonance imaging in real time (FIRE): Sliding‐window correlation analysis and reference‐vector optimization , 2000, Magnetic resonance in medicine.

[43]  Daniel Gembris,et al.  Functional Magnetic Resonance Imaging in Real-Time (FIRE) , 2000 .

[44]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[45]  M. Lowe,et al.  Functional Connectivity in Single and Multislice Echoplanar Imaging Using Resting-State Fluctuations , 1998, NeuroImage.

[46]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[47]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[48]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[49]  Karl J. Friston,et al.  Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets , 1993, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[50]  L. Squire,et al.  The Neuropsychology of Memory , 1990 .

[51]  M K Habib,et al.  Dynamics of neuronal firing correlation: modulation of "effective connectivity". , 1989, Journal of neurophysiology.

[52]  T Hamanaka,et al.  [Neuropsychology of memory]. , 1985, No to shinkei = Brain and nerve.

[53]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[54]  Xi Jiang,et al.  Meta-Analysis of Functional Roles of DICCCOLs , 2012, Neuroinformatics.

[55]  Stefan Köhler,et al.  Differential contributions of the parahippocampal place area and the anterior hippocampus to human memory for scenes , 2002, Hippocampus.

[56]  B. Knowlton,et al.  Learning and memory functions of the Basal Ganglia. , 2002, Annual review of neuroscience.

[57]  G Buzsáki,et al.  Spatial organization of physiological activity in the hippocampal region: relevance to memory formation. , 1990, Progress in brain research.

[58]  Michael Elad,et al.  Submitted to Ieee Transactions on Image Processing Image Decomposition via the Combination of Sparse Representations and a Variational Approach , 2022 .