Cybernetics and Computer Engineering, 2022, 2(208)
PANAGIOTIS KATRAKAZAS, Ph.D., Research Area Manager
ILIAS SPAIS, Ph.D., Senior Project Manager
Researcher ID: https://www.semanticscholar.org/author/I.-Spais/1885927
14452, Metamorfosi, Athens, GR
BLUEPRINTS ELICITATION FRAMEWORK FOR AN OPEN ACCESS PAN-EUROPEAN NEURO-IMAGING ONLINE CENTRE
Introduction. Recent infrastructural endeavours in the field of neuroscience aimed at data integration and sharing and availability of research output. This approach recognized that opening experimental results produces significant gains for science advancement. Nonetheless, this leaves a large part of the grassroots neuroscience community underutilized: access to neuroimaging infrastructures remains locally restricted, obstructing data acquisition and the means to investigate novel hypotheses.
Purpose. Within our paper we seek to address this gap by providing the blueprints for a delocalized e-neuroscience centre, opening the access to functional neuroimaging acquisition systems at a pan-European level. This aim will be achieved by building operational interoperability, standardizing, and integrating the services of neuroscience centres across Europe and the development of a virtual environment allowing all European researchers to acquire state-of-the-art neuroimaging data, exploiting the principles of the European Charter for Access to Research Infrastructures
Results. The implementation of all necessary actions for the harmonization and interoperability of the experimental procedures of the labs entail standardization of protocols, procedures in the form of consensus-based guidelines, harmonization of hardware and software set-up and availability across laboratories, as well as adopting of common standards and formats for acquired data and metadata structures.
Conclusion. Consistent and streamlined mobility processes aim to become a blueprint for networking of the overall neuroscience community. The harmonized process framework presented in this paper can facilitate better use from current and future neuroscience projects. Data economies of scale and recruitment streamlining will put local EU and international funds to better use than the now dispersed efforts. This will lead to more successful projects and better pacing for EU neuroscientific communities in the international stage.
Keywords: multi-centre interoperability, operational harmonisation, neuroimaging, sharing infrastructures, open access framework.
1. Ravindranath V. et al., ‘Regional research priorities in brain and nervous system disorders’, Nature, vol. 527, no. 7578, Art. no. 7578, Nov. 2015, doi: 10.1038/nature16036.
2. GBD 2015 Neurological Disorders Collaborator Group, ‘Global, regional, and national burden of neurological disorders during 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015’, Lancet Neurol., vol. 16, no. 11, pp. 877–897, Nov. 2017, doi: 10.1016/S1474-4422(17)30299-5.
3. Vos T. et al., ‘Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013’, The Lancet, vol. 386, no. 9995, pp. 743–800, Aug. 2015, doi: 10.1016/S0140-6736(15)60692-4.
4. De Domenico M., Granell C., Porter M.A., and Arenas A., ‘The physics of spreading processes in multilayer networks’, Nat. Phys., vol. 12, no. 10, pp. 901–906, Oct. 2016, doi: 10.1038/nphys3865.
5. Battiston F., Nicosia V., and Latora V., ‘The new challenges of multiplex networks: Measures and models’, Eur. Phys. J. Spec. Top., vol. 226, no. 3, pp. 401–416, Feb. 2017, doi: 10.1140/epjst/e2016-60274-8.
6. Paraskevopoulos E., Chalas N., Anagnostopoulou A., and Bamidis P.D., ‘Interaction within and between cortical networks subserving multisensory learning and its reorganization due to musical expertise’, Sci. Rep., vol. 12, no. 1, Art. no. 1, May 2022, doi: 10.1038/s41598-022-12158-9.
7. Mantzavinos V. and Alexiou A., ‘Biomarkers for Alzheimer’s Disease Diagnosis’, Curr. Alzheimer Res., vol. 14, no. 11, pp. 1149–1154, 2017, doi: 10.2174/1567205014666170203125942.
8. Larson-Prior L.J. et al., ‘Adding dynamics to the Human Connectome Project with MEG’, NeuroImage, vol. 80, pp. 190–201, Oct. 2013, doi: 10.1016/j.neuroimage.2013.05.056.
9. Dottori M. et al., ‘Towards affordable biomarkers of frontotemporal dementia: A classification study via network’s information sharing’, Sci. Rep., vol. 7, no. 1, Art. no. 1,
Jun. 2017, doi: 10.1038/s41598-017-04204-8.
10. Iakovidou N.D., ‘Graph Theory at the Service of Electroencephalograms’, Brain Connect., vol. 7, no. 3, pp. 137–151, Apr. 2017, doi: 10.1089/brain.2016.0426.
11. Shabir M.Y., Iqbal A., Mahmood Z., and Ghafoor A., ‘Analysis of classical encryption techniques in cloud computing’, Tsinghua Sci. Technol., vol. 21, no. 1, pp. 102–113, Feb. 2016, doi: 10.1109/TST.2016.7399287.
12. Lingwei S., Fang Y., Ru Z., and Xinxin N., ‘Method of secure, scalable, and fine-grained data access control with efficient revocation in untrusted cloud’, J. China Univ. Posts Telecommun., vol. 22, no. 2, pp. 38–43, Apr. 2015, doi: 10.1016/S1005-8885(15)60637-9.
13. Lee K., Lee D.H., and Park J.H., ‘Efficient revocable identity-based encryption via subset difference methods’, Des. Codes Cryptogr., vol. 85, no. 1, pp. 39–76, Oct. 2017, doi: 10.1007/s10623-016-0287-3.
14. Pasupuleti S.K. and Varma D., ‘Chapter 5 — Lightweight ciphertext-policy attribute-based encryption scheme for data privacy and security in cloud-assisted IoT’, in Real-Time Data Analytics for Large Scale Sensor Data, vol. 6, H. Das, N. Dey, and V. Emilia Balas, Eds. Academic Press, 2020, pp. 97–114. doi: 10.1016/B978-0-12-818014-3.00005-X.
15. Warsinske J. et al., The Official (ISC)2 Guide to the CISSP CBK Reference. John Wiley & Sons, 2019.
16. Zhou L., Fu A., Yu S., Su M., and Kuang B., ‘Data integrity verification of the outsourced big data in the cloud environment: A survey’, J. Netw. Comput. Appl., vol. 122, pp. 1–15, Nov. 2018, doi: 10.1016/j.jnca.2018.08.003.
17. Khraisat A., Gondal I., Vamplew P., and Kamruzzaman J., ‘Survey of intrusion detection systems: techniques, datasets and challenges’, Cybersecurity, vol. 2, no. 1, p. 20, Jul. 2019, doi: 10.1186/s42400-019-0038-7.
18. Nor’a M.N.A. and Ismail A.W., ‘Integrating Virtual Reality and Augmented Reality in a Collaborative User Interface’, Int. J. Innov. Comput., vol. 9, no. 2, Art. no. 2, Nov. 2019, doi: 10.11113/ijic.v9n2.242.
19. Sarasvuo S., Rindell A., and Kovalchuk M., ‘Toward a conceptual understanding of co-creation in branding’, J. Bus. Res., vol. 139, pp. 543–563, Feb. 2022, doi: 10.1016/j.jbusres.2021.09.051.
20. Saarijärvi H., ‘The mechanisms of value co-creation’, J. Strateg. Mark., vol. 20, no. 5, pp. 381–391, Aug. 2012, doi: 10.1080/0965254X.2012.671339.
21. Rizzo F., Deserti A., and Komatsu T., ‘Implementing social innovation in real contexts’, Int. J. Knowl.-Based Dev., vol. 11, no. 1, pp. 45–67, 2020.
22. Frow P., Nenonen S., Payne A., and Storbacka K., ‘Managing Co-creation Design:
A Strategic Approach to Innovation’, Br. J. Manag., vol. 26, no. 3, pp. 463–483, 2015, doi: 10.1111/1467-8551.12087.
23. Martínez-Cañas R., Ruiz-Palomino P., Linuesa-Langreo J., and Blázquez-Resino J.J., ‘Consumer Participation in Co-creation: An Enlightening Model of Causes and Effects Based on Ethical Values and Transcendent Motives’, Front. Psychol., vol. 7, 2016, Accessed: May 21, 2022. [Online]. Available: https://www.frontiersin.org/article/10.3389/fpsyg.2016.00793
24. Jansma S.R., Dijkstra A.M., and de Jong M.D.T., ‘Co-creation in support of responsible research and innovation: an analysis of three stakeholder workshops on nanotechnology for health’, J. Responsible Innov., vol. 9, no. 1, pp. 28–48, Jan. 2022, doi: 10.1080/23299460.2021.1994195.