Incorporating Regional Brain Connectivity Profiles into the Inference of Exposure-Related Neurobehavioral Burden in Explosive Ordnance Disposal Veterans | SpringerLink
Skip to main content
Springer Nature Link
Log in

Incorporating Regional Brain Connectivity Profiles into the Inference of Exposure-Related Neurobehavioral Burden in Explosive Ordnance Disposal Veterans

  • Conference paper
  • First Online:
Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management (HCII 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14710))

Included in the following conference series:

  • 347 Accesses

Abstract

Conventional data-driven investigation into novel and complex health outcomes is often impeded by sparse and incomplete data fragmented across platforms and studies. To address this, we propose a knowledge-driven framework for assembling mechanistically informed regulatory networks and simulating their dynamic behavior. We apply the Natural Language Processing (NLP) engine MedScan to the Elsevier ontology and full text corpus (>7.2 million journal articles) to extract documented relationships linking 40 bilateral Brodmann Area (BA) regions, to 9 self-reported neurobehavioral measures of mood, quality of life, resilience and symptom burden. A Constraint Programming problem was defined to determine the direction and mode of action for each network interaction as well as logic parameters describing signal transmission thresholds and decisional weights dictating each node’s state transition. Parameter values were identified such that the predicted behavior of this integrated neurobehavioral regulatory network would jointly explain 1) two distinct neurobehavioral profiles associated with subjective military exposure histories in a pilot cohort (n = 13) of deployed Explosive Ordnance Disposal (EOD) veterans, and 2) EEG regional source activation patterns (theta/alpha spectral power ratio) previously reported during acute mild Traumatic Brain Injury (acute mTBI), chronic mild-moderate Traumatic Brain Injury (chronic mmTBI), as well as control subjects from a publicly available database. Outcomes from the resulting family of competing models unanimously predicted distinct shifts in neural activity in the dorsolateral prefrontal cortex (Brodmann Areas 9 and 46) presenting primarily in the right hemisphere for self-reported exposure to ionizing radiation sources and in the left hemisphere in the case of non-ionizing radiation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 7549
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 9437
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Frueh, B.C., et al.: “Operator syndrome”: a unique constellation of medical and behavioral health-care needs of military special operation forces. Int. J. Psychiatry Med. 55(4), 281–295 (2020)

    Article  Google Scholar 

  2. Stewart, W., Trujillo, K.: Modern Warfare Destroys Brains. Paper, Belfer Center for Science and International Affairs, Harvard Kennedy School. https://www.belfercenter.org/sites/default/files/2020-07/ModernWarfareDestroysBrains.pdf. Accessed 20 Dec 2023

  3. Khan, A.A., Chaudhari, O., Chandra, R.: A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation. Expert Syst. Appl. 244(15), 122778 (2024)

    Article  Google Scholar 

  4. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)

    Article  Google Scholar 

  5. Lahat, D., Adali, T., Jutten, C.: Multimodal data fusion: an overview of methods, challenges, and prospects. Proc. IEEE 103(9), 1449–1477 (2015)

    Article  Google Scholar 

  6. Kortemme, T., Baker, D.: Computational design of protein-protein interactions. Curr. Opin. Chem. Biol. 8(1), 91–97 (2004)

    Article  Google Scholar 

  7. Gangemi, A., Mika, P.: Understanding the semantic web through descriptions and situations. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) OTM 2003. LNCS, vol. 2888, pp. 689–706. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39964-3_44

  8. Ali, I.M.: Ontology-driven semantic data integration in open environment. Electronic Thesis and dissertation repository, 7230 (2020). https://ir.lib.uwo.ca/etd/7230. https://www.sciencedirect.com/science/article/pii/S0957417423032803. Accessed 12 Dec 2023

  9. Holmes, T.H., Rahe, R.H.: The social readjustment rating scale. J. Psychosom. Res. 11(2), 213–218 (1967)

    Article  Google Scholar 

  10. Connor, K.M., Davidson, J.R.T.: Development of a new resilience scale: the connor-davidson resilience scale(CD-RISC). Depress. Anxiety 18(2), 76–82 (2003)

    Article  Google Scholar 

  11. Lee, R.M., Draper, M., Lee, S.: Social connectedness, dysfunctional interpersonal behaviors, and psychological distress: testing a mediator model. J. Couns. Psychol. 48(3), 310–318 (2001)

    Article  Google Scholar 

  12. Baer, R.A., et al.: Construct validity of the five facet mindfulness questionnaire in meditating and nonmeditating samples. Assessment 15(3), 329–342 (2008)

    Article  Google Scholar 

  13. Cicerone, K.D., Kalmar, K.: Persistent postconcussion syndrome: the structure of subjective complaints after mild traumatic brain injury. J. Head Trauma Rehabil. 10(3), 1–17 (1995)

    Article  Google Scholar 

  14. Cella, D., et al.: Neuro-QOL: brief measures of health-related quality of life for clinical research in neurology. Neurology 78(23), 1860–1867 (2012)

    Article  Google Scholar 

  15. Blevins, C.A., Weathers, F.W., Davis, M.T., Witte, T.K., Domino, J.L.: The posttraumatic stress disorder checklist for DSM-5 (PCL-5): development and initial psychometric evaluation. J. Trauma Stress 28(6), 489–498 (2015)

    Article  Google Scholar 

  16. Buysse, D.J., Reynolds, C.F., Monk, T.H., Berman, S.R., Kupfer, D.J.: The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 28(2), 193–213 (1989)

    Article  Google Scholar 

  17. Kroenke, K., Spitzer, R.L.: The phq-9: A new depression diagnostic and severity measure. Psychiatr. Ann. 32(9), 509–515 (2002)

    Article  Google Scholar 

  18. Hamerly, G., Drake, J.: Accelerating Lloyd’s algorithm for k-means clustering. In: Celebi, M. (ed.) Partitional Clustering Algorithms, pp. 41–78. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-09259-1_2

  19. Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Charlottesville, August 2001, pp. 269–274. Association for Computing Machinery (ACM) (2001)

    Google Scholar 

  20. Samuel, I. et al.: Effects of military occupational exposures on home-based assessment of veterans’ self-reported health, sleep and cognitive performance measures. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) HCII 2022. LNCS, vol. 13310, pp. 91–102. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05457-0_8

  21. Kamdar, M.R., et al.: Text snippets to corroborate medical relations: an unsupervised approach using a knowledge graph and embeddings. In: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, pp. 288–297 (2020)

    Google Scholar 

  22. Nikitin, A., Egorov, S., Daraselia, N., Mazo, I.: Pathway studio—the analysis and navigation of molecular networks. Bioinformatics 19(16), 2155–2157 (2003)

    Article  Google Scholar 

  23. Yuryev, A.: Targeting transcription factors in cell regulation. Expert Opin. Ther. Targets 10(3), 345–349 (2006)

    Article  Google Scholar 

  24. Novichkova, S., Egorov, S., Daraselia, N.: MedScan, a natural language processing engine for MEDLINE abstracts. Bioinformatics 19(13), 1699–1706 (2003)

    Article  Google Scholar 

  25. Daraselia, N., Yuryev, A., Egorov, S., Novichkova, S., Nikitin, A., Mazo, I.: Extracting human protein interactions from MEDLINE using a full-sentence parser. Bioinformatics 20(5), 604–611 (2004)

    Article  Google Scholar 

  26. Markiewicz, C.J., et al.: The OpenNeuro resource for sharing of neuroscience data. Elife 10, e71774 (2021)

    Article  Google Scholar 

  27. Cavanagh, J.F., Quinn, D.: EEG: three-stim auditory oddball and rest in acute and chronic TBI. OpenNeuro. Dataset (2021). https://doi.org/10.18112/openneuro.ds003522.v1.1.0. Accessed 12 Dec 2023

  28. Delorme, A., et al.: NEMAR: an open access data, tools and compute resource operating on neuroelectromagnetic data. Database 2022, baac096 (2022)

    Google Scholar 

  29. Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004)

    Article  Google Scholar 

  30. Tadel, F., Baillet, S., Mosher, J.C., Pantazis, D., Leahy, R.M.: Brainstorm: a user-friendly application for MEG/EEG analysis. Comput. Intell. Neurosci. 2011, 879716 (2011)

    Article  Google Scholar 

  31. Kothe, C.A., Makeig, S.: BCILAB: a platform for brain-computer interface development. J. Neural Eng. 10(5), 056014 (2013)

    Article  Google Scholar 

  32. Lee, T.W., Girolami, M., Sejnowski, T.J.: Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Comput. 11(2), 417–441 (1999)

    Article  Google Scholar 

  33. Winkler, I., Haufe, S., Tangermann, M.: Automatic classification of artifactual ICA-components for artifact removal in EEG signals. Behav. Brain Funct. 7, 1–15 (2011)

    Article  Google Scholar 

  34. Pascual-Marqui, R.D.: Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find. Exp. Clin. Pharmacol. 24(Suppl. D), 5–12 (2002)

    Google Scholar 

  35. Van Essen, D.C.: A population-average, landmark-and surface-based (PALS) atlas of human cerebral cortex. Neuroimage 28(3), 635–662 (2005)

    Article  Google Scholar 

  36. Trammell, J.P., MacRae, P.G., Davis, G., Bergstedt, D., Anderson, A.E.: The relationship of cognitive performance and the theta-alpha power ratio is age-dependent: an EEG study of short term memory and reasoning during task and resting-state in healthy young and old adults. Front. Aging Neurosci. 9, 364 (2017)

    Article  Google Scholar 

  37. Zhao, Y., Wong, L., Goh, W.W.B.: How to do quantile normalization correctly for gene expression data analyses. Sci. Rep. 10(1), 15534 (2020)

    Article  Google Scholar 

  38. Amjad, A.M., Halliday, D.M., Rosenberg, J.R., Conway, B.A.: An extended difference of coherence test for comparing and combining several independent coherence estimates: theory and application to the study of motor units and physiological tremor. J. Neurosci. Methods 73(1), 69–79 (1997)

    Article  Google Scholar 

  39. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  40. Thomas, R.: Regulatory networks seen as asynchronous automata: a logical description. J. Theor. Biol. 153, 1–23 (1991)

    Article  Google Scholar 

  41. Mendoza, L., Xenarios, I.: A method for the generation of standardized qualitative dynamical systems of regulatory networks. Theor. Biol. Med. Model. 3, 13 (2006)

    Article  Google Scholar 

  42. Sedghamiz, H., Morris, M., Craddock, T.J.A., Whitley, D., Broderick, G.: High-fidelity discrete modeling of the HPA axis: a study of regulatory plasticity in biology. BMC Syst. Biol. 12(1), 76 (2018)

    Article  Google Scholar 

  43. Barták, R.: Constraint programming: in pursuit of the holy grail. Theor. Comput. Sci. 17(12), 555–564 (1999)

    Google Scholar 

  44. Sedghamiz, H., Chen, W., Rice, M., Whitley, D., Broderick, G.: Selecting optimal models based on efficiency and robustness in multi-valued biological networks. In: 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), October 2017, pp. 200–205. IEEE, New York (2017)

    Google Scholar 

  45. Sedghamiz, H., Morris, M., Craddock, T.J.A., Whitley, D., Broderick, G.: Bio-ModelChecker: using bounded constraint satisfaction to seamlessly integrate observed behavior with prior knowledge of biological networks. Front. Bioeng. Biotechnol. 7, 48 (2019)

    Article  Google Scholar 

  46. Guns, T.: Increasing modeling language convenience with a universal n-dimensional array, CPpy as python- embedded example. In: The 18th Workshop on Constraint Modelling and Reformulation (ModRef 2019), University of Connecticut, Stanmford (2019)

    Google Scholar 

  47. Stuckey, P.J.: Lazy clause generation: combining the power of SAT and CP (and MIP?) solving. In: Lodi, A., Milano, M., Toth, P. (eds.) CPAIOR 2010. LNCS, vol. 6140, pp. 5–9. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_3

  48. Cuvelier, T., Didier, F., Furnon, V., Gay, S., Mohajeri, S., Perron, L.: OR-tools’ vehicle routing solver: a generic constraint-programming solver with heuristic search for routing problems. In: 24e congrès annuel de la société française de recherche opérationnelle et d'aide à la décision, ROADEF, Rennes, France, ⟨hal-04015496⟩ (2023)

    Google Scholar 

  49. Liffiton, M.H., Sakallah, K.A.: Algorithms for computing minimal unsatisfiable subsets of constraints. J. Autom. Reason. 40(1), 1–33 (2008)

    Article  MathSciNet  Google Scholar 

  50. Bleukx, I., Devriendt, J., Gamba, E., Bogaerts, B., Guns, T.: Simplifying step-wise explanation sequences. In: 29th International Conference on Principles and Practice of Constraint Programming (CP 2023), vol. 280, no. 11, pp. 11:1–11:20. Schloss Dagstuhl-Leibniz-Zentrum für Informatik (2023)

    Google Scholar 

  51. Gamba, E., Bogaerts, B., Guns, T.: Efficiently explaining CSPs with unsatisfiable subset optimization. J. Artif. Intell. Res. 78, 709–746 (2023)

    Article  MathSciNet  Google Scholar 

  52. Vashishtha, S., Broderick, G., Craddock, T.J., Fletcher, M.A., Klimas, N.G.: Inferring broad regulatory biology from time course data: have we reached an upper bound under constraints typical of in vivo studies? PLoS ONE 10(5), e0127364 (2015)

    Article  Google Scholar 

  53. Feder, A., et al.: Causal inference in natural language processing: estimation, prediction, interpretation and beyond. Trans. Assoc. Comput. Linguist. 10, 1138–1158 (2022)

    Article  Google Scholar 

  54. Keith, K.A., Jensen, D., O'Connor, B.: Text and causal inference: a review of using text to remove confounding from causal estimates. arXiv preprint arXiv:2005.00649 (2020)

  55. Hassanzadeh, O., et al.: Answering binary causal questions through large-scale text mining: an evaluation using cause-effect pairs from human experts. In: 2019 International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao, pp. 5003–5009 (2019)

    Google Scholar 

  56. Le Frioux, L., Baarir, S., Sopena, J., Kordon, F.: PaInleSS: a framework for parallel SAT solving. In: Gaspers, S., Walsh, T. (eds.) SAT 2017. LNCS, vol. 10491, pp. 233–250. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66263-3_15

  57. Campbell, E., Khurana, A., Montanaro, A.: Applying quantum algorithms to constraint satisfaction problems. Quantum 3, 167 (2019)

    Article  Google Scholar 

  58. Videla, S., et al.: Designing experiments to discriminate families of logic models. Front. Bioeng. Biotechnol. 3, 131 (2015)

    Article  Google Scholar 

  59. Pang, E.W.: Different neural mechanisms underlie deficits in mental flexibility in post-traumatic stress disorder compared to mild traumatic brain injury. Front. Psych. 6, 170 (2015)

    Google Scholar 

  60. Eierud, C., et al.: Neuroimaging after mild traumatic brain injury: review and meta-analysis. NeuroImage Clin. 4, 283–294 (2014)

    Google Scholar 

Download references

Acknowledgments

This work was supported by Rochester Regional Health in conjunction with Elsevier BV (Amsterdam) under a collaborative research sponsorship (Broderick, PI) and the US Department of Veterans Affairs through an Interagency Personnel Agreement (IPA) (Broderick, Chacko, Page) award. Pathway Studio (© 2020), Elsevier Text Mining and Elsevier Knowledge Graph are trademarks of Elsevier Limited. Copyright Elsevier Limited except certain content provided by third parties.

Mandatory Disclosure.

The opinions and assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting the views of the US Department of Veterans Affairs, the US Department of Defense, Rochester Regional Health, or Elsevier BV.

Disclosure of Interests.

The authors have no competing interests to declare that are relevant to the content of this article.

Author information

Authors and Affiliations

  1. Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, USA

    Jeffrey Page, Thomas Chacko & Gordon Broderick

  2. Department of Kinesiology, University of Maryland, College Park, MD, USA

    Hyuk Oh

  3. Department of Veterans Affairs, War Related Illness and Injury Study Center, Washington, DC, USA

    Immanuel B. H. Samuel, Calvin Lu, Robert D. Forsten, Matthew J. Reinhard & Michelle E. Costanzo

  4. Department of Veterans Affairs, Complex Exposures Threats Center, Washington, DC, USA

    Thomas Chacko, Immanuel B. H. Samuel, Calvin Lu, Robert D. Forsten, Matthew J. Reinhard, Michelle E. Costanzo & Gordon Broderick

  5. Uniformed Services University, Washington, DC, USA

    Robert D. Forsten & Michelle E. Costanzo

Authors
  1. Jeffrey Page
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hyuk Oh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Chacko
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Immanuel B. H. Samuel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Calvin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Robert D. Forsten
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Matthew J. Reinhard
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Michelle E. Costanzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Gordon Broderick
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gordon Broderick .

Editor information

Editors and Affiliations

  1. Purdue University, West Lafayette, IN, USA

    Vincent G. Duffy

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Page, J. et al. (2024). Incorporating Regional Brain Connectivity Profiles into the Inference of Exposure-Related Neurobehavioral Burden in Explosive Ordnance Disposal Veterans. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2024. Lecture Notes in Computer Science, vol 14710. Springer, Cham. https://doi.org/10.1007/978-3-031-61063-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-61063-9_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-61062-2

  • Online ISBN: 978-3-031-61063-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation