The tenth annual MLSP competition: Schizophrenia classification challenge

IEEE International Workshop on Machine Learning for Signal Processing, MLSP

Volumn , Issue , 2014, Pages

  Silva, Rogers F ^a,b   Castro, Eduardo ^a   Gupta, Cota Navin ^a   Cetin, Mustafa ^a,b   Arbabshirani, Mohammad ^a,b   Potluru, Vamsi K ^a,b   Plis, Sergey M ^a   Calhoun, Vince D ^a,b  

^a MIND RESEARCH NETWORK   (United States)

^b UNIVERSITY OF NEW MEXICO   (United States)

Author keywords

Competition; FNC; MRI; SBM; Schizophrenia

Indexed keywords

COMPETITION; COMPUTER AIDED DIAGNOSIS; DISEASES; MAGNETIC RESONANCE IMAGING; NEUROIMAGING;

BRAIN MRI; CLASSIFICATION TASKS; MRI SCAN; MULTIMODAL FEATURES; PREDICTION-BASED; SCHIZOPHRENIA; WINNING STRATEGY;

MACHINE LEARNING;

EID: 84912551879     PISSN: 21610363     EISSN: 21610371     Source Type: Conference Proceeding    
DOI: 10.1109/MLSP.2014.6958889     Document Type: Conference Paper

References (27)

1. A. Solin, and S. Särkkä, "The 10th Annual MLSP Competition: First Place," in IEEE MLSP, Reims, France, 2014, In Press.
2. A. V. Lebedev, "The 10th Annual MLSP Competition: Second Place," in IEEE MLSP, Reims, France, 2014, In Press.
3. K. Koncevičius, "The 10th Annual MLSP Competition: Third Place," in IEEE MLSP, Reims, France, 2014, In Press.
4. C. Bois, H. Whalley, A. McIntosh et al., "Structural magnetic resonance imaging markers of susceptibility and transition to schizophrenia: A review of familial and clinical high risk population studies," Journal of Psychopharmacology, 2014.
5. O. Demirci, and V. D. Calhoun, "Functional Magnetic Resonance Imaging - Implications for Detection of Schizophrenia," Eur Neurol Rev, vol. 4, no. 2, pp. 103-106, 2009.
6. M. J. Jafri, G. D. Pearlson, M. Stevens et al., "A method for functional network connectivity among spatially independent resting-state components in schizophrenia," NeuroImage, vol. 39, no. 4, pp. 1666-1681, 2008.
7. M. R. Arbabshirani, K. Kiehl, G. Pearlson et al., "Classification of schizophrenia patients based on resting-state functional network connectivity," Frontiers in Neuroscience, vol. 7, 2013.
8. J. M. Segall, J. A. Turner, T. G. van Erp et al., "Voxel-based morphometric multisite collaborative study on schizophrenia," Schizophr Bull, vol. 35, no. 1, pp. 82-95, 2009.
9. J. A. Turner, V. D. Calhoun, A. Michael et al., "Heritability of Multivariate Gray Matter Measures in Schizophrenia," Twin Res Hum Genet, vol. 15, no. Special Issue 03, pp. 324-335, 2012.
10. C. N. Gupta, V. D. Calhoun, S. Rachakonda et al., "Patterns of Gray Matter Loss in Schizophrenia from a Large-Scale Aggregated Dataset," Schizophrenia Bulletin, Submitted.
11. E. Castro, C. N. Gupta, M. Martinez-Ramon et al., "Identification of Patterns of Gray Matter Abnormalities in Schizophrenia Using Source-Based Morphometry and Bagging," in 36th EMBC, Chicago, IL, 2014, In Press.
12. S. M. Plis, D. Hjelm, R. Salakhutdinov et al., "Deep learning for neuroimaging: a validation study," Front Neurosci, vol.8, 2014.
13. M. Nieuwenhuis, N. E. M. van Haren, H. E. Hulshoff Pol et al., "Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples," NeuroImage, vol. 61, no. 3, pp. 606-612, 2012.
14. E. A. Allen, E. B. Erhardt, E. Damaraju et al., "A Baseline for the Multivariate Comparison of Resting-State Networks," Front Syst Neurosci, vol. 5, no. 2, 2011.
15. J. M. Segall, E. A. Allen, R. E. Jung et al., "Correspondence between Structure and Function in the Human Brain at Rest," Frontiers in Neuroinformatics, vol. 6, 2012.
16. E. B. Erhardt, S. Rachakonda, E. J. Bedrick et al., "Comparison of Multi-Subject ICA Methods for Analysis of fMRI Data," Human Brain Mapping, vol.32, no.12, pp. 2075-2095, 2011.
17. L. Freire, A. Roche, and J. F. Mangin, "What is the best similarity measure for motion correction in fMRI time series?," IEEE Trans Med Imaging, vol. 21, no. 5, pp. 470-484, 2002.
18. J. Ashburner, and K. J. Friston, "Voxel-based morphometry- the methods," NeuroImage, vol. 11, no. 6 Pt 1, pp. 805-821, 2000.
19. E. Allen, E. Erhardt, et al., "Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study," NeuroImage, vol.59, no.4, pp.4141-4159, 2012.
20. V. D. Calhoun, and T. Adali, "Multisubject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery," IEEE Reviews in Biomedical Engineering, vol. 5, pp. 60-73, 2012.
21. S. Robinson, G. Basso, N. Soldati et al., "A resting state network in the motor control circuit of the basal ganglia," BMC Neuroscience, vol. 10, no. 1, pp. 137, 2009.
22. D. Cordes, V. M. Haughton, K. Arfanakis et al., "Mapping functionally related regions of brain with functional connectivity MR imaging," Am J Neurorad, vol.21, no.9, pp.1636-1644, 2000.
23. L. Xu, K. M. Groth, G. Pearlson et al., "Source-based morphometry: The use of independent component analysis to identify gray matter differences with application to schizophrenia," Human Brain Mapping, vol. 30, no. 3, pp. 711-724, 2009.
24. C. N. Gupta, V. D. Calhoun, S. Rachakonda et al., "Application of Source Based Morphometry for an Aggregated Gray Matter Dataset," in 14th ICSR, Grande Lakes, FL, 2013.
25. J. Eng, "Receiver Operating Characteristic Analysis: A Primer," Academic Radiology, vol. 12, no. 7, pp. 909-916, 2005.
26. R. Polikar, "Ensemble based systems in decision making," Circuits and Systems Magazine, IEEE, vol.6, no.3, pp.21-45, 2006.
27. L. I. Kuncheva, Combining pattern classifiers : methods and algorithms, 2 ed., Hoboken, NJ: Wiley, 2014.