Abstract
With the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks. In this study, we implement the neuromark pipeline in COINSTAC, an open-source neuroimaging framework for collaborative/decentralized analysis. Decentralized exploratory analysis of nearly 2000 resting-state functional magnetic resonance imaging datasets collected at different sites across two cohorts and co-located in different countries was performed to study the resting brain functional network connectivity changes in adolescents who smoke and consume alcohol. Results showed hypoconnectivity across the majority of networks including sensory, default mode, and subcortical domains, more for alcohol than smoking, and decreased low frequency power. These findings suggest that global reduced synchronization is associated with both tobacco and alcohol use. This proof-of-concept work demonstrates the utility and incentives associated with large-scale decentralized collaborations spanning multiple sites.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
References
Allen, E. A., Erhardt, E. B., Damaraju, E., Gruner, W., Segall, J. M., Silva, R. F., Havlicek, M., Rachakonda, S., Fries, J., Kalyanam, R., et al. (2011). A baseline for the multivariate comparison of resting-state networks. Frontiers in Systems Neuroscience, 5, 2.
Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., & Buckner, R. L. (2010). Functional-anatomic fractionation of the brain’s default network. Neuron, 65, 550–562.
Baker, B. T., Damaraju, E., Silva, R. F., Plis, S. M., & Calhoun, V. D. (2020). Decentralized dynamic functional network connectivity: State analysis in collaborative settings. Human Brain Mapping.
Baker, B. T., Silva, R. F., Calhoun, V. D., Sarwate, A. D., & Plis, S. M. (2015). Large scale collaboration with autonomy: Decentralized data ica. In 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1–6). IEEE.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57, 289–300.
Calhoun, V. D., Liu, J., & Adalı, T. (2009). A review of group ica for fmri data and ica for joint inference of imaging, genetic, and erp data. Neuroimage, 45, S163–S172.
Camchong, J., Stenger, A., & Fein, G. (2012). Resting-State Synchrony During Early Alcohol Abstinence Can Predict Subsequent Relapse. Cerebral Cortex, 23, 2086–2099.
Du, Y., Allen, E., He, H., Sui, J., Wu, L., & Calhoun, V. (2016). Artifact removal in the context of group ica: A comparison of single-subject and group approaches. Human Brain Mapping, 37, 1005–1025.
Du, Y., & Fan, Y. (2013). Group information guided ica for fmri data analysis. Neuroimage, 69, 157–197.
Du, Y., Fu, Z., Sui, J., Gao, S., Xing, Y., Lin, D., Salman, M., Abrol, A., Rahaman, M. A., Chen, J., Hong, L. E., Kochunov, P., Osuch, E. A., & Calhoun, V. D. (2020). NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. NeuroImage: Clinical, 28, 102375.
Ebner, T. J., & Pasalar, S. (2008). Cerebellum predicts the future motor state. The Cerebellum, 7, 583–588.
Eickhoff, S., Nichols, T. E., Horn, J. D. V., & Turner, J. A. (2016). Sharing the wealth: Neuroimaging data repositories. NeuroImage, 124, 1065–1068.
Fedota, J. R., & Stein, E. A. (2015). Resting-state functional connectivity and nicotine addiction: prospects for biomarker development. Annals of the New York Academy of Sciences, 1349, 64.
Gazula, H., Baker, B., Damaraju, E., Plis, S. M., Panta, S. R., Silva, R. F., & Calhoun, V. D. (2018). Decentralized analysis of brain imaging data: Voxel-based morphometry and dynamic functional network connectivity. Frontiers in Neuroinformatics, 12, 55.
Gazula, H., Holla, B., Zhang, Z., Xu, J., Verner, E., Kelly, R., Jain, S., Bharath, R. D., Barker, G. J., Basu, D., Chakrabarti, A., Kalyanram, K., Kumaran, K., Singh, L., Kuriyan, R., Murthy, P., Benega, V., Plis, S. M., Sarwate, A. D., Turner, J. A., Schumann, G., & Calhoun, V. D. (2021). Decentralized multisite VBM analysis during adolescence shows structural changes linked to age, body mass index, and smoking: a COINSTAC analysis. Neuroinformatics.
Gazula, H., Kelly, R., Romero, J., Verner, E., Baker, B. T., Silva, R. F., Imtiaz, H., Saha, D. K., Raja, R., Turner, J. A., et al. (2020). Coinstac: Collaborative informatics and neuroimaging suite toolkit for anonymous computation. Journal of Open Source Software, 5, 2166.
Heatherton, T. F., Kozlowski, L. T., Frecker, R. C., & FAGERSTROM, K. -O. (1991). The fagerström test for nicotine dependence: a revision of the fagerstrom tolerance questionnaire. British Journal of Addiction, 86, 1119–1127.
Holmes, A. J., Hollinshead, M. O., O’keefe, T. M., Petrov, V. I., Fariello, G. R., Wald, L. L., Fischl, B., Rosen, B. R., Mair, R. W., Roffman, J. L., et al. (2015). Brain genomics superstruct project initial data release with structural, functional, and behavioral measures. Scientific Data, 2, 1–16.
Jansen, J. M., van Holst, R. J., van den Brink, W., Veltman, D. J., Caan, M. W., & Goudriaan, A. E. (2015). Brain function during cognitive flexibility and white matter integrity in alcohol-dependent patients, problematic drinkers and healthy controls. Addiction biology, 20, 979–989.
Lin, Q. -H., Liu, J., Zheng, Y. -R., Liang, H., & Calhoun, V. D. (2010). Semiblind spatial ica of fmri using spatial constraints. Human Brain Mapping, 31, 1076–1088.
Ming, J., Verner, E., Sarwate, A., Kelly, R., Reed, C., Kahleck, T., Silva, R., Panta, S., Turner, J., Plis, S., et al. (2017). Coinstac: Decentralizing the future of brain imaging analysis. F1000Research, 6, 1512.
Plis, S. M., Sarwate, A. D., Wood, D., Dieringer, C., Landis, D., Reed, C., Panta, S. R., Turner, J. A., Shoemaker, J. M., Carter, K. W., et al. (2016). Coinstac: a privacy enabled model and prototype for leveraging and processing decentralized brain imaging data. Frontiers in Neuroscience, 10, 365.
Popa, L. S., & Ebner, T. J. (2019). Cerebellum, predictions and errors. Frontiers in Cellular Neuroscience, 12, 524.
Pujol, J., Blanco-Hinojo, L., Batalla, A., López-Solà, M., Harrison, B. J., Soriano-Mas, C., Crippa, J. A., Fagundo, A. B., Deus, J., De la Torre, R., et al. (2014). Functional connectivity alterations in brain networks relevant to self-awareness in chronic cannabis users. Journal of Psychiatric Research, 51, 68–78.
Saha, D. K., Calhoun, V. D., Du, Y., Fu, Z., Panta, S. R., Kwon, S., Sarwate, A., & Plis, S. M. (2021). Privacy-preserving quality control of neuroimaging datasets in federated environment. bioRxiv, (p. 826974).
Saha, D. K., Calhoun, V. D., Panta, S. R., & Plis, S. M. (2017). See without looking: joint visualization of sensitive multi-site datasets. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI’2017) (pp. 2672–2678). Melbourne, Australia.
Salman, M. S., Wager, T. D., Damaraju, E., Abrol, A., Vergara, V. M., Fu, Z., & Calhoun, V. D. (2021). An approach to automatically label and order brain activity/component maps. Brain Connectivity.
Sarwate, A. D., Plis, S. M., Turner, J. A., Arbabshirani, M. R., & Calhoun, V. D. (2014). Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation. Frontiers in Neuroinformatics, 8, 35.
Schumann, G., Loth, E., Banaschewski, T., Barbot, A., Barker, G., Büchel, C., Conrod, P., Dalley, J., Flor, H., Gallinat, J., et al. (2010). The imagen study: reinforcement-related behaviour in normal brain function and psychopathology. Molecular Psychiatry, 15, 1128.
Sharma, E., Vaidya, N., Iyengar, U., Zhang, Y., Holla, B., Purushottam, M., Chakrabarti, A., Fernandes, G. S., Heron, J., Hickman, M., Desrivieres, S., Kartik, K., Jacob, P., Rangaswamy, M., Bharath, R. D., Barker, G., Orfanos, D. P., Ahuja, C., Murthy, P., Jain, S., Varghese, M., Jayarajan, D., Kumar, K., Thennarasu, K., Basu, D., Subodh, B. N., Kuriyan, R., Kurpad, S. S., Kalyanram, K., Krishnaveni, G., Krishna, M., Singh, R. L., Singh, L. R., Kalyanram, K., Toledano, M., Schumann, G., & Benegal, V. (2020). Consortium on vulnerability to externalizing disorders and addictions (cVEDA): A developmental cohort study protocol. BMC Psychiatry, 20.
Shringarpure, S. S., & Bustamante, C. D. (2015). Privacy risks from genomic data-sharing beacons. The American Journal of Human Genetics, 97, 631–646.
Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10, 557–570.
Vergara, V. M., Liu, J., Claus, E. D., Hutchison, K., & Calhoun, V. (2017). Alterations of resting state functional network connectivity in the brain of nicotine and alcohol users. NeuroImage, 151, 45–54.
White, T., Blok, E., & Calhoun, V. D. (2020). Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed. Human Brain Mapping.
Wilcox, C. E., Brett, M. E., & Calhoun, V. D. (2020). Objective markers for psychiatric decision-making: How to move imaging into clinical practice. NeuroImage: Clinical, 26, 102084.
Zhang, Y., Vaidya, N., Iyengar, U., Sharma, E., Holla, B., Ahuja, C. K., Barker, G. J., Basu, D., Bharath, R. D., Chakrabarti, A., Desrivieres, S., Elliott, P., Fernandes, G., Gourisankar, A., Heron, J., Hickman, M., Jacob, P., Jain, S., Jayarajan, D., Kalyanram, K., Kartik, K., Krishna, M., Krishnaveni, G., Kumar, K., Kumaran, K., Kuriyan, R., Murthy, P., Orfanos, D. P., Purushottam, M., Rangaswamy, M., Kupard, S. S., Singh, L., Singh, R., Subodh, B. N., Thennarasu, K., Toledano, M., Varghese, M., Benegal, V., & Schumann, G. (2020). The consortium on vulnerability to externalizing disorders and addictions (c-VEDA): an accelerated longitudinal cohort of children and adolescents in India. Molecular Psychiatry, 25, 1618–1630.
Zhu, X., Cortes, C. R., Mathur, K., Tomasi, D., & Momenan, R. (2017). Model-free functional connectivity and impulsivity correlates of alcohol dependence: a resting-state study. Addiction Biology, 22, 206–217.
Acknowledgements
This work was funded by the National Institutes of Health (grants: R01DA040487, R01MH121246, R01DA049238, and 1R01DA040487) and the National Science Foundation (grant: 2112455). This work received support from the following sources: Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA) is jointly funded by the Indian Council for Medical Research (ICMR/MRC/3/M/2015-NCD-I) and the Newton Grant from the Medical Research Council(MR/N000390/1), United Kingdom, the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT- 2007-037286), the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (695313), Human Brain Project (HBP SGA 2, 785907, and HBP SGA 3, 945539), the National Institute of Health (NIH) (R01DA049238, A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers), the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministeriumfür Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B; Forschungsnetz IMAC-Mind 01GL1745B), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2, SFB 940, TRR 265, NE 1383/14-1), the Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 - MRF-058-0004-RG-DESRI and MR/S020306/1 - MRF-058-0009-RG-DESR-C0759), the National Institutes of Health (NIH) funded ENIGMA (grants 5U54EB020403-05 and 1R56AG058854-01). Further support was provided by grants from: the ANR (ANR-12-SAMA-0004, AAPG2019 - GeBra), the Eranet Neuron (AF12-NEUR0008-01 - WM2NA; and ANR-18-NEUR00002-01 - ADORe), the Fondation de France (00081242), the Fondation pour la Recherche Médicale (DPA20140629802), the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012, the Fondation de l’Avenir (grant AP-RM-17-013 ), the Fédération pour la Recherche sur le Cerveau; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797), U.S.A. (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1), and by NIH Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. ImagenPathways “Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways” is a collaborative project supported by the European Research Area Network on Illicit Drugs (ERANID). This paper is based on independent research commissioned and funded in England by the National Institute for Health Research (NIHR) Policy Research Programme (project ref. PR-ST-0416-10001). The views expressed in this article are those of the authors and not necessarily those of the national funding agencies or ERANID. Author ZZ received a fellowship from the Medical Research Foundation (MRF-058-0014-F-ZHAN-C0866).
Author information
Authors and Affiliations
Consortia
Contributions
HG led the manuscript writing with support from KR, BH and SB. BH contributed data and was instrumental in performing the analysis in COINSTAC. ZZ contributed data to the study as well as contributed to describing the data. EV and RK manage the COINSTAC project. SP, AD and JT are part of the COINSTAC team. VB and GS are the principal investigators of the CVEDA-IMAGEN consortium and have been instrumental in facilitating this multi-site study. VC is also a co-investigator and leads the COINSTAC team, formed the vision for this work, and helped interpret the results. All others who were not mentioned here are part of either the cVEDA or IMAGEN consortia.
Corresponding authors
Ethics declarations
Conflict of Interest
Dr. Banaschewski served in an advisory or consultancy role for Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, Shire. He received conference support or speaker’s fee by Lilly, Medice, Novartis and Shire. He has been involved in clinical trials conducted by Shire & Viforpharma. He received royalties from Hogrefe, Kohlhammer, CIP Medien, Oxford University Press. The present work is unrelated to the above grants and relationships. Dr. Barker has received honoraria from General Electric Healthcare for teaching on scanner programming courses. Dr. Poustka served in an advisory or consultancy role for Roche and Viforpharm and received speaker’s fee by Shire. She received royalties from Hogrefe, Kohlhammer and Schattauer. The present work is unrelated to the above grants and relationships. The other authors report no biomedical financial interests or potential conflicts of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gazula, H., Rootes-Murdy, K., Holla, B. et al. Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains. Neuroinform 21, 287–301 (2023). https://doi.org/10.1007/s12021-022-09604-4
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12021-022-09604-4