DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders
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.
KeywordsFunctional 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
The authors report no competing interests.
- A. P. Association, Diagnostic and Statistical Manual of Mental Disorders: DSM IV, 4 ed., Washington DC, 1994.Google Scholar
- 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
- 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
- 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
- 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
- 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
- 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.
- 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.
- 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.Google Scholar
- 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
- Kraskov, A., Stogbauer, H., & Grassberger, P. (2004). Estimating mutual information. Physical Review - statistical, nonlinear, and soft matter physics, 69(6), 066138.Google Scholar
- 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
- N. I. o. M. Health. (2019). Analysis of functional NeuroImages, NIH, [Online]. Available: https://afni.nimh.nih.gov/.
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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, 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
- 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