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Neuroimaging - References

Spatio-temporal dynamics in brain imaging

  1. Loss of consciousness is associated with stabilization of cortical activity, G. Solovey, T. Yanagawa, N. Fuji,M.O. Magnasco, G.A. Cecchi \& A. Proekt, J. Neurosci. (2015).
  2. Dynamical criticality during induction of anesthesia in human ECoG recordings, L. Alonso, A. Proekt, T.H. Schwartz, K.O. Pryor, G. A. Cecchi and M.O. Magnasco, Front. Neural Circuits 8:20 (2014).
  3. Linking human brain local activity fluctuations to structural and functional network architectures A.T Baria, A. Mansour, L. Huang, M.N. Baliki, G.A. Cecchi, M.M. Mesulam \& A.V. Apkarian, Neuroimage 73, 144-155 (2013).
  4. Self-regulated dynamical criticality in human ECoG, G. Solovey, K.J. Miller, J. Ojemann, M.O Magnasco \& G.A. Cecchi, Frontiers in Integrative Neuroscience 6:44 (2012).
  5. Full-brain Auto-Regressive Modeling (FARM) using fMRI, R. Garg, G.A. Cecchi. \& A.R. Rao, Neuroimage 58, 416-441 (2011).
  6. Self-tuned critical anti-hebbian networks, M.O. Magnasco, O. Piro \& G.A. Cecchi, Physical Review Letters 102, 258102 (2009).
  7. Ordered cyclic motifs contribute to the dynamic stability of biological and engineered networks, G.A. Cecchi, A. Ma'ayan, J. Wagner, A.R. Rao, R. Iyengar & G. Stolovitzky, Proceedings of the National Academy of Sciences USA 105, 19235-40 (2008).
  8. Identifying directed links in large scale functional networks: application to brain fMRI, G.A. Cecchi, A.R. Rao, M.V. Centeno, M. Baliki, A.V. Apkarian \& D.R. Chialvo, BMC Cell Biology 8 (Suppl 1):S5 (2007).
  9. Scale-free brain functional networks, V.M. Eguiluz, D.R. Chialvo. G.A. Cecchi, M. Baliki, A.V. Apkarian, Physical Review Letters 94, 018102 (2005).

Schizophrenia: network-based discriminative models

  1. Irina Rish, Guillermo Cecchi, Benjamin Thyreau, Bertrand Thirion, Marion Plaze, Marie Laure Paillere-Martinot, Catherine Martelli, Jean-Luc Martinot, Jean-Baptiste Poline. Schizophrenia as a Network Disease: Disruption of Emergent Brain Function in Patients with Auditory Hallucinations. PloS one 8(1), e50625, Public Library of Science, 2013.
  2. Irina Rish, Guillermo A Cecchi, Kyle Heuton. Schizophrenia classification using functional network features. SPIE Medical Imaging, pp. 83170W--83170W, 2012.
  3. Guillermo Cecchi, Irina Rish, Benjamin Thyreau, Bertrand Thirion, Marion Plaze, Marie-Laure Paillere-Martinot, Catherine Martelli, Jean-Luc Martinot, Jean-Baptiste Poline. Discriminative network models of schizophrenia. Advances in Neural Information Processing Systems (NIPS 2009) , pp. 252--260, 2009. 

EEG-based cognitive load prediction with deep convolutional networks

  1. Pouya Bashivan, Irina Rish, Mohammed Yeasin, Noel Codella. Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks. ICLR 2016: International Conference on Learning Representations 2016.  

Wearable EEG-based mental state recognition

  1. Pouya Bashivan, Irina Rish, Steve Heisig. Mental State Recognition via Wearable EEG. NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI2015), 2015.  

Pain perception modeling

  1. Guillermo A Cecchi, Lejian Huang, Javeria Ali Hashmi, Marwan Baliki, Maria V Centeno, Irina Rish, A Vania Apkarian. Predictive Dynamics of Human Pain Perception. PLoS computational biology 8(10), e1002719, Public Library of Science, 2012.
  2. Irina Rish, Guillermo A Cecchi, Kyle Heuton, Marwan N Baliki, A Vania Apkarian. Sparse regression analysis of task-relevant information distribution in the brain. SPIE Medical Imaging, 2012
  3. Irina Rish, Guillermo A Cecchi, Marwan N Baliki, A Vania Apkarian. Sparse regression models of pain perception. Brain Informatics, pp. 212--223, Springer, 2010.


Cocaine addiction: Multivariate predictive modeling

  1. Irina Rish, Pouya Bashivan, Guillermo A. Cecchi, Rita Z. Goldstein. Evaluating Effects of Methylphenidate on Brain Activity in Cocaine Addiction: A Machine-Learning Approach. SPIE Medical Imaging, 2016.
  2. Jean Honorio, Dimitris Samaras, Irina Rish, Guillermo Cecchi. Variable Selection for Gaussian Graphical Models. AISTATS, 2012


 Sparse models and interpretable fMRI Analysis 

  1. Carroll, Melissa K., Guillermo A. Cecchi, Irina Rish, Rahul Garg, Marwan Baliki, and A. Vania Apkarian. Reliability Estimation and Enhancement via Spatial Smoothing in Sparse fMRI Modeling. Practical Applications of Sparse Modeling, pp. 123-150, MIT Press, 2014.
  2. Melissa K Carroll, Guillermo A Cecchi, Irina Rish, Rahul Garg, A Ravishankar Rao. Prediction and interpretation of distributed neural activity with sparse models. NeuroImage 44(1), 112--122, Elsevier, 2009.