DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders

  • Mohammed A. Syed
  • Zhi Yang
  • D. Rangaprakash
  • Xiaoping Hu
  • Michael N. Dretsch
  • Jeffrey S. Katz
  • Thomas S. DenneyJr
  • Gopikrishna DeshpandeEmail author
Software Original Article


There is a lack of objective biomarkers to accurately identify the underlying etiology and related pathophysiology of disparate brain-based disorders that are less distinguishable clinically. Brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been a popular tool for discovering candidate biomarkers. Specifically, independent component analysis (ICA) of rs-fMRI data is a powerful multivariate technique for investigating brain networks. However, ICA-derived brain networks that are not highly reproducible within heterogeneous clinical populations may exhibit mean statistical separation between groups, yet not be sufficiently discriminative at the individual-subject level. We hypothesize that functional brain networks that are most reproducible in subjects within clinical and control groups separately, but not when the two groups are merged, may possess the ability to discriminate effectively between the groups even at the individual-subject level. In this study, we present DisConICA or “Discover Confirm Independent Component Analysis”, a software package that implements the methodology in support of our hypothesis. It relies on a “discover-confirm” approach based upon the assessment of reproducibility of independent components (representing brain networks) obtained from rs-fMRI (discover phase) using the gRAICAR (generalized Ranking and Averaging Independent Component Analysis by Reproducibility) algorithm, followed by unsupervised clustering analysis of these components to evaluate their ability to discriminate between groups (confirm phase). The unique feature of our software package is its ability to seamlessly interface with other software packages such as SPM and FSL, so that all related analyses utilizing features of other software can be performed within our package, thus providing a one-stop software solution starting with raw DICOM images to the final results. We showcase our software using rs-fMRI data acquired from US Army soldiers returning from the wars in Iraq and Afghanistan who were clinically grouped into the following groups: PTSD (posttraumatic stress disorder), comorbid PCS (post-concussion syndrome) + PTSD, and matched healthy combat controls. This software package along with test data sets is available for download at


Functional MRI Independent component analysis Reproducibility Clustering Posttraumatic stress disorder Post-concussion syndrome 



The authors would like to thank National Science Foundation - NSF (Grant # 0966278) for funding the author MA Syed during this study. The authors acknowledge financial support for data acquisition from the U.S. Army Medical Research and Materials Command (MRMC) (Grant # 00007218, PI: M. Dretsch). Material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the author, and are not to be construed as official, or as reflecting true views of the Department of the Army or the Department of Defense. The investigators have adhered to the policies for protection of human subjects as prescribed in AR 70–25. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank the personnel at the TBI clinic and behavioral health clinic, Fort Benning, GA, USA and the US Army Aeromedical Research Laboratory, Fort Rucker, AL, USA, and most of all, the soldiers who participated in the study. The authors thank Julie Rodiek and Wayne Duggan for facilitating data acquisition and Adam Goodman and Thomas Daniel for assistance in data collection. The author MA Syed is not representing the Boeing Company through this article. Author Z Yang is funded by the National Science Foundation of China (81270023, PI: Z. Yang), Foundation of Beijing Key Laboratory of Mental Disorders (2014JSJB03, PI: Z. Yang), Beijing Nova Program for Science and Technology (XXJH2015B079, PI: Z. Yang), and The Outstanding Young Investigator Award of Institute of Psychology, Chinese Academy of Sciences (Y4CX062008, PI: Z. Yang). X Hu and G Deshpande are supported in part by NIH (DA033393) and NIH (R01EY025978), respectively.

Compliance with Ethical Standards

Competing Interests

The authors report no competing interests.


  1. A. P. Association, Diagnostic and Statistical Manual of Mental Disorders: DSM IV, 4 ed., Washington DC, 1994.Google Scholar
  2. Abdallah, C., Wrocklage, K., Averill, C., Akiki, T., Schweinsburg, B., Roy, A., Martini, B., Southwick, S., Krystal, J., & Scott, J. (2017). Anterior hippocampal dysconnectivity in posttraumatic stress disorder: A dimensional and multimodal approach. Translational Psychiatry, 7(2), e1045.Google Scholar
  3. Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics, 8(14), 8–14.Google Scholar
  4. Adams, K., Hester, P., Bradley, J., Meyers, T., & Keating, C. (2013). Systems theory as the Foundation for Understanding Systems. Syst Eng, 17(1), 112–123.CrossRefGoogle Scholar
  5. Allen, E., Damaraju, E., Plis, S., Erhardt, E., Eichele, T., & Calhoun, V. (2014). Tracking whole-brain connectivity dynamics in the resting state. Cerebral Cortex, 24(3), 663–676.CrossRefGoogle Scholar
  6. Assaf, M., Jagannathan, K., Calhoun, V., Miller, L., Stevens, M., Sahl, R., O'Boyle, J., Schultz, R., & Pearlson, G. (2010). Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients. Neuroimage, 53(1), 247–256.Google Scholar
  7. Baker, M. (2016). 1,500 scientists lift the lid on reproducibility. Nature, 533(7604), 452–454.CrossRefGoogle Scholar
  8. Beckman, C., & Smith, S. (2004). Probabilistic independent component analysis. IEEE Transactions on Medical Imaging, 23, 137–152.CrossRefGoogle Scholar
  9. Beckman, C., DeLuca, M., Devlin, J., & Smith, S. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society, 360(1457), 1001–1013.Google Scholar
  10. Bell, A., & Sejnowski, T. (1995). An information maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129–1159.CrossRefGoogle Scholar
  11. Bellec, P., Rosa-Neto, P., Lyttelton, O., Benali, H., & Evans, A. (2010). Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage, 1126–1139.Google Scholar
  12. Calhoun, V., & Adali, T. (2017). Group ICA Of fMRI Toolbox(GIFT), Medical Image Analysis Lab, [Online]. Available:
  13. Camchong, J., MacDonald, A., III, Nelson, B., Bell, C., Mueller, B., Specker, S., & Lim, K. (2011). Frontal hyperconnectivity related to discounting and reversal learning in cocaine subjects. Biological Psychiatry, 69(11), 1117–1123.CrossRefGoogle Scholar
  14. Chai, X., Nieto-Castanon, A., Ongur, D., & Whitfield-Gabrieli, S. (2012). Anticorrelations in resting state networks without global signal regression. NeuroImage, 59(2), 1420–1428.CrossRefGoogle Scholar
  15. Chalmers, I., & Glasziou, P. (2009). Avoidable waste in the production and reporting of research evidence. The Lancet, 374, 86–89.CrossRefGoogle Scholar
  16. Choi, E., Tanimura, Y., Vage, P., Yates, E., & Haber, S. (2017). Convergence of prefrontal and parietal anatomical projections in a connectional hub in the striatum. Neuroimage, 146, 821–832.CrossRefGoogle Scholar
  17. Cisler, J., Scott, S. J., Smitherman, S., Lenow, J., & Kilts, C. (2013). Neural processing correlates of assaultive violence exposure and PTSD symptoms during implicit threat processing: A network-level analysis among adolescent girls. Psychiatry Research, 214(3), 238–246.CrossRefGoogle Scholar
  18. Davey, C., Harrison, B., Yücel, M., & Allen, N. (2012). Regionally specific alterations in functional connectivity of the anterior cingulate cortex in major depressive disorder. Psychological Medicine, 42(10), 2071–2081.CrossRefGoogle Scholar
  19. DiGangi, J., Tadayyon, A., Fitzgerald, D., Rabinak, C., Kennedy, A., Klumpp, H., Rauch, S., & Phan, K. (2016). Reduced default mode network connectivity following combat trauma. Neuroscience Letters, 615, 37–43.CrossRefGoogle Scholar
  20. Dretsch, M. N., Rangaprakash, D., Katz, J. S., Daniel, T. A., Goodman, A. M., Denney, T. S., & Deshpande, G. (2019). Strength and temporal variance of the default mode network to investigate chronic mild traumatic brain injury in service members with psychological trauma. Journal of Experimental Neuroscience, 13.
  21. Goebel, R. (2019). BrainVoyager, Brain Innovation B.V., 2015. [Online]. Available:
  22. Gorgolewski, K., & Poldrack, R. (2016). A practical guide for improving transparency and reproducibility in neuroimaging research. PLoS Biology, 14(7), e1002506.Google Scholar
  23. Greicius, M., Krasnow, B., Reiss, A., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences (PNAS), 100(1), 253–258.Google Scholar
  24. Hoge, C., Castro, C., Messer, S., McGurk, D., Cotting, D., & Koffman, R. (2004). Combat duty in Iraq and Afghanistan, mental health problems, and barriers to care. The New England Journal of Medicine, 351, 13–22.CrossRefGoogle Scholar
  25. Jenkinson, M., Beckmann, C., Behrens, T., Woolrich, M., & Smith, S. (2012). The FMRIB software library (FSL). Neuroimage, 62(2), 782–790.CrossRefGoogle Scholar
  26. Kaczkurkin, A., Burton, P., Chazin, S., Manbeck, A., Espensen-Sturges, T., Cooper, S., Sponheim, S., & Lissek, S. (2017). Neural substrates of overgeneralized conditioned fear in PTSD. The American Journal of Psychiatry, 174(2), 125–134.CrossRefGoogle Scholar
  27. Kelly, A., Di Martino, A., Uddin, L., Shehzad, Z., Gee, D., Reiss, P., Margulies, D., Castellanos, F., & Milham, M. (2009). Development of anterior cingulate functional connectivity from late childhood to early adulthood. Cerebral Cortex, 19(3), 640–657.CrossRefGoogle Scholar
  28. Kennis, M., Rademaker, A., van Rooij, S., Kahn, R., & Geuze, E. (2015). Resting state functional connectivity of the anterior cingulate cortex in veterans with and without post-traumatic stress disorder. Human Brain Mapping, 36(1), 99–109.CrossRefGoogle Scholar
  29. Kraskov, A., Stogbauer, H., & Grassberger, P. (2004). Estimating mutual information. Physical Review - statistical, nonlinear, and soft matter physics, 69(6), 066138.Google Scholar
  30. Liu, W., Awate S. and Fletcher, P. "Group analysis of resting-state fMRI by hierarchical Markov random fields," Medical Image Computing and Computer-Assisted Intervention - Lecturer Notes in Computer Science, 2012.Google Scholar
  31. Macleod, M., Michie, S., Roberts, I., Dirnagl, U., Chalmers, I., Ioannidis, J., Salman, R., Chan, A., & Glasziou, P. (2014). Biomedical research: Increasing value, reducing waste. The Lancet, 383(9912), 101–104.CrossRefGoogle Scholar
  32. Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P. (1997). Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging, 16(2), 187–198.CrossRefGoogle Scholar
  33. Moher, D., Glasziou, P., Chalmers, I., Nasser, M., Bossuyt, P., Korevaar, D., Graham, I., Ravaud, P., & Boutron, I. (2016). Increasing value and reducing waste in biomedical research: who's listening? The Lancet, 387, 1573–1586.CrossRefGoogle Scholar
  34. N. I. o. M. Health. (2019). Analysis of functional NeuroImages, NIH, [Online]. Available:
  35. Patriat, R., Birn, R., Keding, T., & Herringa, R. (2016). Default-mode network abnormalities in pediatric posttraumatic stress disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 55(4), 319–327.CrossRefGoogle Scholar
  36. Pitman, R., Rasmusson, A., Koenen, K., Shin, L., Orr, S., Gilbertson, M., Milad, M., & Liberzon, I. (2012). Biological studies of post-traumatic stress disorder. National Reviews Neuroscience, 13(11), 769–787.CrossRefGoogle Scholar
  37. Pluim, J., Maintz, J., & Viergever, M. (2003). Mutual-information-based registration of medical images: A survey. IEEE Transactions on Medical Imaging, 22(8), 986–1004.CrossRefGoogle Scholar
  38. Rangaprakash, D., Deshpande, G., Daniel, T., Goodman, A., Robinson, J., Salibi, N., Katz, J., Denney, T., & Dretsch, M. (2017). Compromised hippocampus-striatum pathway as a potential imaging biomarker of mild traumatic brain injury and posttraumatic stress disorder. Human Brain Mapping, 38, 2843–2864.CrossRefGoogle Scholar
  39. Ross, D., Arbuckle, M., Travis, M., Dwyer, J., van Schalkwyk, G., & Ressler, K. (2017). An integrated neuroscience perspective on formulation and treatment planning for posttraumatic stress disorder: An educational review. JAMA Psychiatry, 74(4), 407–415.Google Scholar
  40. Schulz, J., Cookson, M., & Hausmann, L. (2016). The impact of fraudulent and irreproducible data to the translational research crisis – Solutions and implementation. Journal of Neurochemistry, 139(S2), 253–270.CrossRefGoogle Scholar
  41. Shang, J., Lui, S., Meng, Y., Zhu, H., Qiu, C., Gong, Q., Liao, W., & Zhang, W. (2014). Alterations in low-level perceptual networks related to clinical severity in PTSD after an earthquake: A resting-state fMRI study. PLoS One, 9(5), e96834.Google Scholar
  42. Shin, L., & Liberzon, I. (2010). The neurocircuitry of fear, stress, and anxiety disorders. Neuropsychopharmacology, 35(1), 169–191.CrossRefGoogle Scholar
  43. Shu, I., Onton, J., Prabhakar, N., O'Connell, R., Simmons, A., & Matthews, S. (2014). Combat veterans with PTSD after mild TBI exhibit greater ERPs from posterior-medial cortical areas while appraising facial features. Journal of Affective Disorders, 155, 234–240.CrossRefGoogle Scholar
  44. Song, X., Dong, Z., Long, X., Li, S., Zuo, X., Zhu, C., He, Y., Yan, C., & Zang, Y. (2011). REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS One, 6(9), e25031.Google Scholar
  45. Sussman, D., Pang, E., Jetly, R., Dunkley, B., & Taylor, M. (2016). Neuroanatomical features in soldiers with post-traumatic stress disorder. BMC Neuroscience, 17.Google Scholar
  46. Syed, M., Yang, Z., Hu, X., & Deshpande, G. (2017). Investigating brain connectomic alterations in autism using the reproducibility of independent components derived from resting state functional MRI data. Frontiers in Neuroscience, 11–459.Google Scholar
  47. Thome, J., Frewen, P., Daniels, J., Densmore M. and Lanius, R. (2014). Altered connectivity within the salience network during direct eye gaze in PTSD. Borderline Personality Disorder and Emotion Dysregulation, 1, 17.
  48. U. Research Imaging Institute (2011), Multi-image Analysis GUI, Research Imaging Institute, University of Texas Health Science Center, [Online]. Available:
  49. von dem Hagen, E., Stoyanova, R., Baron-Cohen, S., & Calder, A. (2012). Reduced functional connectivity within and between 'social' resting state networks in autism spectrum conditions. Social Cognitive and Affective Neuroscience, 8(6), 694–701.CrossRefGoogle Scholar
  50. von Rhein, D., Beckmann, C., Franke, B., Oosterlaan, J., Heslenfeld, D., Hoekstra, P., Hartman, C., Luman, M., Faraone, S., Cools, R., Buitelaar, J., & Mennes, M. (2017). Network-level assessment of reward-related activation in patients with ADHD and healthy individuals. Human Brain Mapping, 38(5), 2359–2369.Google Scholar
  51. Waltzman, D., Soman, S., Hantke, N., Fairchild, J., Kinoshita, L., Wintermark, M., Ashford, J., Yesavage, J., Williams, L., Adamson, M., & Furst, A. (2017). Altered Microstructural Caudate Integrity in Posttraumatic Stress Disorder but Not Traumatic Brain Injury. PLoS One, 12(1).Google Scholar
  52. Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012). Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connectivity, 2(3), 125–141.Google Scholar
  53. Wold, S. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1–3), 37–52.CrossRefGoogle Scholar
  54. Woolrich, M., Jbabdi, S., Patenaude, B., Chappell, M., Makni, S., Behrens, T., Beckmann, C., Jenkinson, M., & Smith, S. (2009). Bayesian analysis of neuroimaging data in FSL. Neuroimage, 45(1 Suppl), 173–186.Google Scholar
  55. Wrocklage, K., Averill, L., Cobb, S. J., Averill, C., Schweinsburg, B., Trejo, M., Roy, A., Weisser, V., Kelly, C., Martini, B., Harpaz-Rotem, I., Southwick, S., Krystal, J., & Abdallah, C. (2017). Cortical thickness reduction in combat exposed U.S. veterans with and without PTSD. European Neuropsychopharmacology, 27, 515–525.CrossRefGoogle Scholar
  56. Xia, M., Wang, J., & He, Y. (2013). BrainNet viewer: A network visualization tool for human brain connectomics. PLoS ONE, 8, e68910.CrossRefGoogle Scholar
  57. Yan, C., & Zang, Y. (2010). DPARSF: A MATLAB toolbox for pipeline data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4, 13.Google Scholar
  58. Yang, Z., LaConte, S., Weng, X., & Hu, X. (2008). Ranking and averaging independent component analysis by reproducibility (RAICAR). Human Brain Mapping, 29(6), 711–725.Google Scholar
  59. Yang, Z., Zuo, X., Wang, P., Li, Z., LaConte, S., Bandettini, P., & Hu, X. (2012). Generalized RAICAR: discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks. Neuroimage, 63(1), 403–414.Google Scholar
  60. Yang, Z., Xu, Y., Xu, T., Hoy, C., Handwerker, D., Chen, G., Northoff, G., Zuo X. and Bandettini, P. (2014). Brain network informed subject community detection in early-onset schizophrenia. Scientific Reports, 4, 5549.
  61. Yang, Z., Chang, C., Xu, T., Jiang, L., Handwerker, D., Castellanos, F., Milham, M., Bandettini, P., & Zuo, X. (2014b). Connectivity trajectory across lifespan differentiates the precuneus from the default network. Neuroimage, 89, 45–56.CrossRefGoogle Scholar
  62. Yang, L., Baojuan, L., Na, F., Huangsheng, P., Xi, Z., Hongbing, L., & Hong, Y. (2016). Perfusion deficits and functional connectivity alterations in memory-related regions of patients with post-traumatic stress disorder. PLoS One, 11(5).Google Scholar
  63. Young, G. (2017). PTSD in court II: Risk factors, endophenotypes, and biological underpinnings in PTSD. International Journal of Law and Psychiatry, 51, 1–21.CrossRefGoogle Scholar
  64. Zhang, S., & Li, C. (2012). Functional connectivity mapping of the human precuneus by resting state fMRI. NeuroImage, 59(4), 3548–3562.CrossRefGoogle Scholar
  65. Zhang, Y., Xie, B., Chen, H., Li, M., Guo, X., & Chen, H. (2016a). Disrupted resting-state insular subregions functional connectivity in post-traumatic stress disorder. Medicine, 95(27), e4083.Google Scholar
  66. Zhang, Y., Xie, B., Chen, H., Li, M., Liu, F., & Chen, H. (2016b). Abnormal functional connectivity density in post-traumatic stress disorder. Brain Topography, 29(3), 405–411.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Mohammed A. Syed
    • 1
    • 2
    • 3
  • Zhi Yang
    • 4
  • D. Rangaprakash
    • 1
    • 5
  • Xiaoping Hu
    • 6
  • Michael N. Dretsch
    • 7
    • 8
    • 9
  • Jeffrey S. Katz
    • 1
    • 9
    • 10
    • 11
  • Thomas S. DenneyJr
    • 1
    • 9
    • 10
    • 11
  • Gopikrishna Deshpande
    • 1
    • 9
    • 10
    • 11
    • 12
    • 13
    Email author
  1. 1.AU MRI Research Center, Department of Electrical and Computer EngineeringAuburn UniversityAuburnUSA
  2. 2.Department of Computer Science and Software EngineeringAuburn UniversityAuburnUSA
  3. 3.The Boeing CompanySeattleUSA
  4. 4.Key Laboratory of Behavioral Sciences, Institute of PsychologyChinese Academy of SciencesBeijingChina
  5. 5.Department of RadiologyNorthwestern UniversityChicagoUSA
  6. 6.Department of BioengineeringUniversity of California RiversideRiversideUSA
  7. 7.U.S. Army Aeromedical Research LaboratoryFort RuckerUSA
  8. 8.US Army Medical Research Directorate-WestJoint Base Lewis-McCordTacomaUSA
  9. 9.Department of PsychologyAuburn UniversityAuburnUSA
  10. 10.Center for NeuroscienceAuburn UniversityBirminghamUSA
  11. 11.Alabama Advanced Imaging ConsortiumBirminghamUSA
  12. 12.Department of PsychiatryNational Institute of Mental Health and NeurosciencesBangaloreIndia
  13. 13.Center for Health Ecology and Equity ResearchAuburn UniversityAuburnUSA

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