Mixtures of large-scale dynamic functional brain network modes

[1]  J. Gross,et al.  The natural frequencies of the resting human brain: An MEG-based atlas , 2021, NeuroImage.

[2]  M. Woolrich,et al.  Multi-dynamic modelling reveals strongly time-varying resting fMRI correlations , 2021, bioRxiv.

[3]  J. Palva,et al.  Long range temporal correlations (LRTCs) in MEG-data during emerging psychosis: Relationship to symptoms, medication-status and clinical trajectory , 2021, NeuroImage: Clinical.

[4]  P. Hagmann,et al.  The connectome spectrum as a canonical basis for a sparse representation of fast brain activity , 2021, NeuroImage.

[5]  A. Hillebrand,et al.  Abnormal meta-state activation of dynamic brain networks across the Alzheimer spectrum , 2021, NeuroImage.

[6]  Mathieu Bourguignon,et al.  Localization accuracy of a common beamformer for the comparison of two conditions , 2021, NeuroImage.

[7]  M. Woolrich,et al.  Replay bursts in humans coincide with activation of the default mode and parietal alpha networks , 2020, Neuron.

[8]  Olaf Sporns,et al.  Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series , 2020, bioRxiv.

[9]  Michaël Defferrard,et al.  Connectome spectral analysis to track EEG task dynamics on a subsecond scale , 2020, NeuroImage.

[10]  Mark W. Woolrich,et al.  Transient spectral events in resting state MEG predict individual task responses , 2020, NeuroImage.

[11]  Mark W. Woolrich,et al.  The role of transient spectral ‘bursts’ in functional connectivity: A magnetoencephalography study , 2020, NeuroImage.

[12]  Mark W. Woolrich,et al.  Tracking dynamic brain networks using high temporal resolution MEG measures of functional connectivity , 2019, NeuroImage.

[13]  D. Senkowski,et al.  Long-Range Temporal Correlations in Resting State Beta Oscillations are Reduced in Schizophrenia , 2019, Front. Psychiatry.

[14]  Nelson J. Trujillo-Barreto,et al.  The discrete logic of the Brain - Explicit modelling of Brain State durations in EEG and MEG , 2019, bioRxiv.

[15]  Bethany C Routley,et al.  Attenuated Post-Movement Beta Rebound Associated With Schizotypal Features in Healthy People , 2018, Schizophrenia bulletin.

[16]  Mark W. Woolrich,et al.  Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling , 2018, Front. Neurosci..

[17]  Benjamin A. E. Hunt,et al.  Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks , 2018, Nature Communications.

[18]  Mark Stokes,et al.  Temporally Unconstrained Decoding Reveals Consistent but Time-Varying Stages of Stimulus Processing , 2018, bioRxiv.

[19]  Thomas Koenig,et al.  EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review , 2017, NeuroImage.

[20]  Matthew J. Brookes,et al.  Ghost interactions in MEG/EEG source space: A note of caution on inter-areal coupling measures , 2017, NeuroImage.

[21]  Hedvig Kjellström,et al.  Advances in Variational Inference , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Stephen M. Smith,et al.  Brain network dynamics are hierarchically organized in time , 2017, Proceedings of the National Academy of Sciences.

[23]  Dimitri Van De Ville,et al.  The dynamic functional connectome: State-of-the-art and perspectives , 2017, NeuroImage.

[24]  Diego Vidaurre,et al.  Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks , 2017, bioRxiv.

[25]  Kimberlyn A Bailey,et al.  Decline of long-range temporal correlations in the human brain during sustained wakefulness , 2017, Scientific Reports.

[26]  Aurélien Géron,et al.  Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems , 2017 .

[27]  Linda Douw,et al.  Dynamic hub load predicts cognitive decline after resective neurosurgery , 2017, Scientific Reports.

[28]  Mark W. Woolrich,et al.  Measurement of dynamic task related functional networks using MEG , 2017, NeuroImage.

[29]  Andrea Brovelli,et al.  Dynamic Reconfiguration of Visuomotor-Related Functional Connectivity Networks , 2017, The Journal of Neuroscience.

[30]  Geoffrey E. Hinton,et al.  Regularizing Neural Networks by Penalizing Confident Output Distributions , 2017, ICLR.

[31]  Ben Poole,et al.  Categorical Reparameterization with Gumbel-Softmax , 2016, ICLR.

[32]  M. Stevens The contributions of resting state and task-based functional connectivity studies to our understanding of adolescent brain network maturation , 2016, Neuroscience & Biobehavioral Reviews.

[33]  M Corbetta,et al.  A Dynamic Core Network and Global Efficiency in the Resting Human Brain. , 2016, Cerebral cortex.

[34]  Geoffrey E. Hinton,et al.  Layer Normalization , 2016, ArXiv.

[35]  Thomas E. Nichols,et al.  Scanning the horizon: towards transparent and reproducible neuroimaging research , 2016, Nature Reviews Neuroscience.

[36]  Timothy Edward John Behrens,et al.  Task-free MRI predicts individual differences in brain activity during task performance , 2016, Science.

[37]  Mark W. Woolrich,et al.  Spectrally resolved fast transient brain states in electrophysiological data , 2016, NeuroImage.

[38]  Selen Atasoy,et al.  Human brain networks function in connectome-specific harmonic waves , 2016, Nature Communications.

[39]  Samy Bengio,et al.  Generating Sentences from a Continuous Space , 2015, CoNLL.

[40]  P. Fries Rhythms for Cognition: Communication through Coherence , 2015, Neuron.

[41]  Mark W. Woolrich,et al.  A symmetric multivariate leakage correction for MEG connectomes , 2015, NeuroImage.

[42]  Dimitri Van De Ville,et al.  Long-range dependencies make the difference—Comment on “A stochastic model for EEG microstate sequence analysis” , 2015, NeuroImage.

[43]  Wei Gao,et al.  Task‐related modulation of functional connectivity variability and its behavioral correlations , 2015, Human brain mapping.

[44]  Mark W. Woolrich,et al.  Dynamic recruitment of resting state sub-networks , 2015, NeuroImage.

[45]  Matthew J. Brookes,et al.  Resting state MEG oscillations show long-range temporal correlations of phase synchrony that break down during finger movement , 2015, Front. Physiol..

[46]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[47]  Maurizio Corbetta,et al.  Resting-State Temporal Synchronization Networks Emerge from Connectivity Topology and Heterogeneity , 2015, PLoS Comput. Biol..

[48]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

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

[50]  Aaron Kucyi,et al.  Dynamic functional connectivity of the default mode network tracks daydreaming , 2014, NeuroImage.

[51]  P. Geurts,et al.  Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints , 2014, Brain Structure and Function.

[52]  Biyu J. He Scale-free brain activity: past, present, and future , 2014, Trends in Cognitive Sciences.

[53]  Jonathan D. Power,et al.  Intrinsic and Task-Evoked Network Architectures of the Human Brain , 2014, Neuron.

[54]  Mark W. Woolrich,et al.  Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity , 2014, NeuroImage.

[55]  Stephen M Smith,et al.  Fast transient networks in spontaneous human brain activity , 2014, eLife.

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

[57]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[58]  A. Engel,et al.  Intrinsic Coupling Modes: Multiscale Interactions in Ongoing Brain Activity , 2013, Neuron.

[59]  Mark W. Woolrich,et al.  Resting-state fMRI in the Human Connectome Project , 2013, NeuroImage.

[60]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[61]  Viviana Betti,et al.  Natural Scenes Viewing Alters the Dynamics of Functional Connectivity in the Human Brain , 2013, Neuron.

[62]  Hamid Reza Mohseni,et al.  Dynamic state allocation for MEG source reconstruction , 2013, NeuroImage.

[63]  Xiao Liu,et al.  EEG correlates of time-varying BOLD functional connectivity , 2013, NeuroImage.

[64]  Enzo Tagliazucchi,et al.  Dynamic BOLD functional connectivity in humans and its electrophysiological correlates , 2012, Front. Hum. Neurosci..

[65]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.

[66]  Mark W. Woolrich,et al.  Inferring task-related networks using independent component analysis in magnetoencephalography , 2012, NeuroImage.

[67]  M. Corbetta,et al.  A Cortical Core for Dynamic Integration of Functional Networks in the Resting Human Brain , 2012, Neuron.

[68]  M. Corbetta,et al.  Large-scale cortical correlation structure of spontaneous oscillatory activity , 2012, Nature Neuroscience.

[69]  Jessica A. Turner,et al.  Behavioral Interpretations of Intrinsic Connectivity Networks , 2011, Journal of Cognitive Neuroscience.

[70]  Darren Price,et al.  Investigating the electrophysiological basis of resting state networks using magnetoencephalography , 2011, Proceedings of the National Academy of Sciences.

[71]  Matthew J. Brookes,et al.  Measuring functional connectivity using MEG: Methodology and comparison with fcMRI , 2011, NeuroImage.

[72]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[73]  M. Corbetta,et al.  Temporal dynamics of spontaneous MEG activity in brain networks , 2010, Proceedings of the National Academy of Sciences.

[74]  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.

[75]  Shunzheng Yu,et al.  Hidden semi-Markov models , 2010, Artif. Intell..

[76]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[77]  Mark W. Woolrich,et al.  Bayesian analysis of neuroimaging data in FSL , 2009, NeuroImage.

[78]  Cornelis J. Stam,et al.  Dopaminergic modulation of cortico-cortical functional connectivity in Parkinson's disease: An MEG study , 2008, Experimental Neurology.

[79]  Xiaoping Hu,et al.  Ranking and averaging independent component analysis by reproducibility (RAICAR) , 2008, Human brain mapping.

[80]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[81]  Karl J. Friston,et al.  Variational free energy and the Laplace approximation , 2007, NeuroImage.

[82]  Thomas E. Nichols,et al.  Twelfth Annual Meeting of the Organization for Human Brain Mapping , 2006, NeuroImage.

[83]  Michael T. Jurkiewicz,et al.  Post-movement beta rebound is generated in motor cortex: Evidence from neuromagnetic recordings , 2006, NeuroImage.

[84]  C. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[85]  Karl J. Friston,et al.  A free energy principle for the brain , 2006, Journal of Physiology-Paris.

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

[87]  Visa Koivunen,et al.  Identifiability, separability, and uniqueness of linear ICA models , 2004, IEEE Signal Processing Letters.

[88]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[89]  K. Linkenkaer-Hansen,et al.  Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations , 2001, The Journal of Neuroscience.

[90]  R. Leahy,et al.  A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG. , 1999, Physics in medicine and biology.

[91]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[92]  R. Hari,et al.  Functional Segregation of Movement-Related Rhythmic Activity in the Human Brain , 1995, NeuroImage.

[93]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[94]  B.D. Van Veen,et al.  Beamforming: a versatile approach to spatial filtering , 1988, IEEE ASSP Magazine.

[95]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[96]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[97]  P. Welch The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[98]  Andrea Brovelli,et al.  Dynamic reconfiguration of visuomotor-related functional connectivity networks. , 2016, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[99]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[100]  G. Sandini,et al.  Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. , 2009, Brain : a journal of neurology.

[101]  Karl J. Friston Functional and effective connectivity in neuroimaging: A synthesis , 1994 .

[102]  H. Saunders,et al.  Probability, Random Variables and Stochastic Processes (2nd Edition) , 1989 .

[103]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.