In Journal (refereed)
[J1] Kalmady SV, Paul, A.P., Greiner, R., Agrawal, R., Amaresha, A.C., Shivakumar, V., Narayanaswamy, J.C., Greenshaw, A.J., Dursun, S.M., Venkatasubramanian, G. Extending schizophrenia diagnostic model to predict schizotypy in first-degree relatives. npj Schizophrenia, 6:30, 2020.
[J2] Kalmady SV, Greiner R, Agrawal R, Shivakumar V, Narayanaswamy JC, Brown MRG, Greenshaw AJ, Dursun SM, Venkatasubramanian G. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning. NPJ Schizophrenia, Jan 2019.
[J3] B. Sen, N. Borle, R. Greiner, M. Brown. A General Prediction Model for the Detection of ADHD and Autism using Structural and Functional MRI. PLoS One, March 2018.
[J4] S. Liang, W. Deng, Q. Wang, M. Li, M. Juhas, X. Li, R. Greiner, A. Greenshaw, T. Li. Convergence and divergence of neurocognitive patterns in schizophrenia and depression.. Schizophrenia Research, June 2017.
[J5] S.Liang, R. Vega, X. Kong, W. Deng, Q. Wang, X. Ma, M. Li, X. Hu, A. Greenshaw, R. Greiner, T. Li, Neurocognitive graphs of first-episode schizophrenia and major depression based on cognitive features. Neuroscience Bulletin, 2017.
[J6] M. Gheiratmand, I. Rish, G. Cecchi, M. Brown, R. Greiner, P. Bashivan, A. Greenshaw, R. Ramasubbu, S. Dursun. Learning stable and predictive network-based patterns ofschizophrenia and its clinical symptoms. NPJ Schizophrenia, May 2017.
[J7] M. Gheiratmand, I. Rish, G. Cecchi, M. Brown, R. Greiner, P. Bashivan, P. Polosecki, S. Dursun. Learning Discriminative Functional Network Features of Schizophrenia. SPIE Medical Imaging, April 2017.
[J8] S. Ghiassian, P. Jin, M. Brown, R. Greiner. Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism. PLoS One, November 2016.
[J9] R. Vega, T. Sajed, K. Mathewson, K. Khare, P. Pilarski, R. Greiner, G. Sanchez-Ante, J. Antelis. Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals. Artificial Intelligence Research, 6(1), September 2016.
[J10] R. Ramasubbu, M. Brown, F. Cortese, I. Gaxiola, A. Greenshaw, S. Dursun, B. Goodyear, R. Greiner. Accuracy of Automated Classification of Major Depressive Disorder as a Function of Symptom Severity. NeuroImage: Clinical, July 2016.
[J11] G. Sidhu, N. Asgarian, R. Greiner, M. Brown. Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD. Frontiers in Systems Neuroscience, (ed: Michael Milham), 6(74), pp 17, October 2012.
[J12] M. Brown, G. Sidhu, R. Greiner, N. Asgarian, M. Bastani, P. Silverstone, A. Greenshaw, S. Dursun. ADHD-200 Global Competition: Diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements . Frontiers in Systems Neuroscience, (ed: Michael Milham), 6(10.3389/fnsys.2012.00069), September 2012.
In Workshop
[W1] R. Vega, R. Greiner. Finding Effective Ways to (Machine) Learn fMRI-based Classifiers from Multi-Site Data. Machine Learning in Clinical Neuroimaging (MLCN), September 2018
[W2] J. Dakka, P. Bashivan, M. Gheiratmand, I. Rish, S. Jha, R. Greiner. Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks. NIPS workshop on Machine Learning for Health, Nov. 2017.
[W3] S. Kalmady, R. Greiner, R. Agrawal, S. Venkataram, C. Janardhanan, M. Brown, A. Greenshaw, S. Dursun, G. Venkatasubramanian. Improving prediction of schizophrenia from resting-state functional MRI by combining multiple brain parcellation schemes. Alberta Imaging Symposium, May 2017.
[W4] M. Gheiratmand, I. Rish, G. Cecchi, M. Brown, R. Greiner, A. Greenshaw, S. Dursun. Functional Network Patterns as Multivariate Predictors of Symptom Severity in Schizophrenia. 23nd Annual Meeting of the Organization for Human Brain Mapping, May 2017.
[W5] S. Ghiassian, R. Greiner, M. Brown, P. Jin. Learning to Classify Psychiatric Disorders based on fMR Images: Autism vs Healthy and ADHD vs Healthy. Proceedings of the Workshop on Machine Learning and Interpretation in Neuroimaging, November 2013.
Theses
[T1] R. Vega. The challenge of applying machine learning techniques to diagnose schizophrenia using multi-site fMRI data. MSc Thesis, University of Alberta, Computing Science 2017.
[T2] B. Sen. Generalized Prediction Model for Detection of Psychiatric Disorders. MSc Thesis, University of Alberta, Computing Science 2015.
[T3] S. Ghiassian. Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Diagnose ADHD and Autism. MSc Thesis, University of Alberta, Computing Science 2014.
[T4] G. Sidhu. Dimensionality Reduction for fMRI Diagnostic Systems. MSc Thesis, University of Alberta, Computing Science 2012.
Other Categories
[O1] I. Rish, R. Greiner. Computational Psychiatry -- eliminating the guesswork of treating mental illness. In Moods Magazine, September 2015.
In Journal (refereed)
[J1] Kalmady SV, Paul, A.P., Greiner, R., Agrawal, R., Amaresha, A.C., Shivakumar, V., Narayanaswamy, J.C., Greenshaw, A.J., Dursun, S.M., Venkatasubramanian, G. Extending schizophrenia diagnostic model to predict schizotypy in first-degree relatives. npj Schizophrenia, 6:30, 2020.
[J2] Kalmady SV, Greiner R, Agrawal R, Shivakumar V, Narayanaswamy JC, Brown MRG, Greenshaw AJ, Dursun SM, Venkatasubramanian G. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning. NPJ Schizophrenia, Jan 2019.
[J3] B. Sen, N. Borle, R. Greiner, M. Brown. A General Prediction Model for the Detection of ADHD and Autism using Structural and Functional MRI. PLoS One, March 2018.
[J4] S. Liang, W. Deng, Q. Wang, M. Li, M. Juhas, X. Li, R. Greiner, A. Greenshaw, T. Li. Convergence and divergence of neurocognitive patterns in schizophrenia and depression.. Schizophrenia Research, June 2017.
[J5] S.Liang, R. Vega, X. Kong, W. Deng, Q. Wang, X. Ma, M. Li, X. Hu, A. Greenshaw, R. Greiner, T. Li, Neurocognitive graphs of first-episode schizophrenia and major depression based on cognitive features. Neuroscience Bulletin, 2017.
[J6] M. Gheiratmand, I. Rish, G. Cecchi, M. Brown, R. Greiner, P. Bashivan, A. Greenshaw, R. Ramasubbu, S. Dursun. Learning stable and predictive network-based patterns ofschizophrenia and its clinical symptoms. NPJ Schizophrenia, May 2017.
[J7] M. Gheiratmand, I. Rish, G. Cecchi, M. Brown, R. Greiner, P. Bashivan, P. Polosecki, S. Dursun. Learning Discriminative Functional Network Features of Schizophrenia. SPIE Medical Imaging, April 2017.
[J8] S. Ghiassian, P. Jin, M. Brown, R. Greiner. Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism. PLoS One, November 2016.
[J9] R. Vega, T. Sajed, K. Mathewson, K. Khare, P. Pilarski, R. Greiner, G. Sanchez-Ante, J. Antelis. Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals. Artificial Intelligence Research, 6(1), September 2016.
[J10] R. Ramasubbu, M. Brown, F. Cortese, I. Gaxiola, A. Greenshaw, S. Dursun, B. Goodyear, R. Greiner. Accuracy of Automated Classification of Major Depressive Disorder as a Function of Symptom Severity. NeuroImage: Clinical, July 2016.
[J11] G. Sidhu, N. Asgarian, R. Greiner, M. Brown. Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD. Frontiers in Systems Neuroscience, (ed: Michael Milham), 6(74), pp 17, October 2012.
[J12] M. Brown, G. Sidhu, R. Greiner, N. Asgarian, M. Bastani, P. Silverstone, A. Greenshaw, S. Dursun. ADHD-200 Global Competition: Diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements . Frontiers in Systems Neuroscience, (ed: Michael Milham), 6(10.3389/fnsys.2012.00069), September 2012.
In Workshop
[W1] R. Vega, R. Greiner. Finding Effective Ways to (Machine) Learn fMRI-based Classifiers from Multi-Site Data. Machine Learning in Clinical Neuroimaging (MLCN), September 2018
[W2] J. Dakka, P. Bashivan, M. Gheiratmand, I. Rish, S. Jha, R. Greiner. Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks. NIPS workshop on Machine Learning for Health, Nov. 2017.
[W3] S. Kalmady, R. Greiner, R. Agrawal, S. Venkataram, C. Janardhanan, M. Brown, A. Greenshaw, S. Dursun, G. Venkatasubramanian. Improving prediction of schizophrenia from resting-state functional MRI by combining multiple brain parcellation schemes. Alberta Imaging Symposium, May 2017.
[W4] M. Gheiratmand, I. Rish, G. Cecchi, M. Brown, R. Greiner, A. Greenshaw, S. Dursun. Functional Network Patterns as Multivariate Predictors of Symptom Severity in Schizophrenia. 23nd Annual Meeting of the Organization for Human Brain Mapping, May 2017.
[W5] S. Ghiassian, R. Greiner, M. Brown, P. Jin. Learning to Classify Psychiatric Disorders based on fMR Images: Autism vs Healthy and ADHD vs Healthy. Proceedings of the Workshop on Machine Learning and Interpretation in Neuroimaging, November 2013.
Theses
[T1] R. Vega. The challenge of applying machine learning techniques to diagnose schizophrenia using multi-site fMRI data. MSc Thesis, University of Alberta, Computing Science 2017.
[T2] B. Sen. Generalized Prediction Model for Detection of Psychiatric Disorders. MSc Thesis, University of Alberta, Computing Science 2015.
[T3] S. Ghiassian. Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Diagnose ADHD and Autism. MSc Thesis, University of Alberta, Computing Science 2014.
[T4] G. Sidhu. Dimensionality Reduction for fMRI Diagnostic Systems. MSc Thesis, University of Alberta, Computing Science 2012.
Other Categories
[O1] I. Rish, R. Greiner. Computational Psychiatry -- eliminating the guesswork of treating mental illness. In Moods Magazine, September 2015.