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

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Abstract

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 https://bitbucket.org/masauburn/disconica.

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References

  • A. P. Association, Diagnostic and Statistical Manual of Mental Disorders: DSM IV, 4 ed., Washington DC, 1994.

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

  • Baker, M. (2016). 1,500 scientists lift the lid on reproducibility. Nature, 533(7604), 452–454.

    Article  CAS  PubMed  Google Scholar 

  • Beckman, C., & Smith, S. (2004). Probabilistic independent component analysis. IEEE Transactions on Medical Imaging, 23, 137–152.

    Article  Google Scholar 

  • 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.

  • Bell, A., & Sejnowski, T. (1995). An information maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129–1159.

    Article  CAS  PubMed  Google Scholar 

  • 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.

  • Calhoun, V., & Adali, T. (2017). Group ICA Of fMRI Toolbox(GIFT), Medical Image Analysis Lab, [Online]. Available: http://mialab.mrn.org/software/gift/index.html.

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Chalmers, I., & Glasziou, P. (2009). Avoidable waste in the production and reporting of research evidence. The Lancet, 374, 86–89.

    Article  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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. https://doi.org/10.1177/1179069519833966.

    Article  Google Scholar 

  • Goebel, R. (2019). BrainVoyager, Brain Innovation B.V., 2015. [Online]. Available: http://www.brainvoyager.com/products/brainvoyager.html.

  • Gorgolewski, K., & Poldrack, R. (2016). A practical guide for improving transparency and reproducibility in neuroimaging research. PLoS Biology, 14(7), e1002506.

  • 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.

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Jenkinson, M., Beckmann, C., Behrens, T., Woolrich, M., & Smith, S. (2012). The FMRIB software library (FSL). Neuroimage, 62(2), 782–790.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Kraskov, A., Stogbauer, H., & Grassberger, P. (2004). Estimating mutual information. Physical Review - statistical, nonlinear, and soft matter physics, 69(6), 066138.

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • N. I. o. M. Health. (2019). Analysis of functional NeuroImages, NIH, [Online]. Available: https://afni.nimh.nih.gov/.

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

  • Shin, L., & Liberzon, I. (2010). The neurocircuitry of fear, stress, and anxiety disorders. Neuropsychopharmacology, 35(1), 169–191.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

  • Sussman, D., Pang, E., Jetly, R., Dunkley, B., & Taylor, M. (2016). Neuroanatomical features in soldiers with post-traumatic stress disorder. BMC Neuroscience, 17.

  • 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.

  • 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. https://doi.org/10.1186/2051-6673-1-17.

    Article  PubMed  PubMed Central  Google Scholar 

  • U. Research Imaging Institute (2011), Multi-image Analysis GUI, Research Imaging Institute, University of Texas Health Science Center, [Online]. Available: http://rii.uthscsa.edu/mango/mango.html.

  • 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.

    Article  Google Scholar 

  • 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.

  • 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).

  • Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012). Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connectivity, 2(3), 125–141.

  • Wold, S. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1–3), 37–52.

    Article  CAS  Google Scholar 

  • 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.

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xia, M., Wang, J., & He, Y. (2013). BrainNet viewer: A network visualization tool for human brain connectomics. PLoS ONE, 8, e68910.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yan, C., & Zang, Y. (2010). DPARSF: A MATLAB toolbox for pipeline data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4, 13.

  • 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.

  • 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.

  • 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. https://doi.org/10.1038/srep05549.

  • 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.

    Article  PubMed  Google Scholar 

  • 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).

  • Young, G. (2017). PTSD in court II: Risk factors, endophenotypes, and biological underpinnings in PTSD. International Journal of Law and Psychiatry, 51, 1–21.

    Article  PubMed  Google Scholar 

  • Zhang, S., & Li, C. (2012). Functional connectivity mapping of the human precuneus by resting state fMRI. NeuroImage, 59(4), 3548–3562.

    Article  PubMed  Google Scholar 

  • 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.

  • 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.

    Article  PubMed  Google Scholar 

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Acknowledgements

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.

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Authors and Affiliations

  1. AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA

    Mohammed A. Syed, D. Rangaprakash, Jeffrey S. Katz, Thomas S. Denney Jr & Gopikrishna Deshpande

  2. Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA

    Mohammed A. Syed

  3. The Boeing Company, Seattle, WA, USA

    Mohammed A. Syed

  4. Key Laboratory of Behavioral Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, China

    Zhi Yang

  5. Department of Radiology, Northwestern University, Chicago, IL, USA

    D. Rangaprakash

  6. Department of Bioengineering, University of California Riverside, Riverside, CA, USA

    Xiaoping Hu

  7. U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, USA

    Michael N. Dretsch

  8. US Army Medical Research Directorate-West, Joint Base Lewis-McCord, Tacoma, WA, USA

    Michael N. Dretsch

  9. Department of Psychology, Auburn University, Auburn, AL, USA

    Michael N. Dretsch, Jeffrey S. Katz, Thomas S. Denney Jr & Gopikrishna Deshpande

  10. Center for Neuroscience, Auburn University, Birmingham, AL, USA

    Jeffrey S. Katz, Thomas S. Denney Jr & Gopikrishna Deshpande

  11. Alabama Advanced Imaging Consortium, Birmingham, AL, USA

    Jeffrey S. Katz, Thomas S. Denney Jr & Gopikrishna Deshpande

  12. Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India

    Gopikrishna Deshpande

  13. Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA

    Gopikrishna Deshpande

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  1. Mohammed A. Syed
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Syed, M.A., Yang, Z., Rangaprakash, D. et al. DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders. Neuroinform 18, 87–107 (2020). https://doi.org/10.1007/s12021-019-09422-1

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