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Introduction:An unprecedented richness of data and methodologies enables more detailed access to neural processes but also poses the challenge to combine insights across experiments, species, and measurement techniques. While different experimental recording modalities offer complementary views onto the brain, their data analysis approaches and workflows are often too specific to compare the results rigorously. However, this challenge also promises new avenues of scientific progress. By aligning existing data and analyses from different sources in a reusable workflow we can build a broader basis for meta-studies, contextualization of individual studies, and model validation.Here, we showcase such an analysis pipeline with the application to cortical wave activity in the delta (‘slow waves’) and beta range. Cortical waves can be prominently observed in a variety of heterogeneous data [1,2] and a plethora of analytical methods exist that we aim to interface within a consistent framework: the ‘collaborative brain wave analysis pipeline’ (CobraWap).Methods:The design of CobraWap is based on modular building blocks that provide implementations of analysis methods and processing steps. These blocks are grouped in task-specific stages, e.g., data entry, data processing, trigger detection, wave detection, wave characterization. By letting the pipeline match the input and output format requirements for each of these pipeline components, defining a workflow becomes a matter of selecting a combination of stages and blocks to be applied. This flexibility is employed to converge the heterogeneous data to a common description level of wave activity, from which then common characteristic measures, such as velocity, direction, inter-wave intervals, or wave type classifications, can be derived and quantitatively compared across the data. We demonstrate the versatility of the pipeline with multiple datasets of ECoG [3] and calcium imaging recordings [4] of anesthetized mice, and Utah-array recordings of awake behaving macaques [e.g. 5]. Further, we integrate standard analysis methods from the literature to serve the requirements of a wide range of datasets and research questions. To emphasize the reusability and extendability of each of the pipeline components, the pipeline builds entirely on open-source solutions, such as the workflow manager Snakemake (RRID:SCR_003475), the Neo (RRID:SCR_000634) library for data representation [6], the Elephant (RRID:SCR_003833) analysis toolbox, and the EBRAINS Knowledge Graph (https://kg.ebrains.eu) for capturing outputs of the pipeline execution.Results:The pipeline design promotes the creation of application-tailored and reproducible analysis workflows for many datasets. We demonstrate this “big-data'' approach by investigating dataset-specific parameters across different experiments. For example, we evaluate the influences of the type and dose of anesthesia or the measurement modality and their temporal and spatial resolution on the characteristics of slow waves (e.g., wave velocities) and show that we can replicate corresponding findings from the literature [7,8,9,10].Just as applying the same methods to different data enables a fair comparison between datasets, the pipeline equally enables analyzing the same data with different methods to benchmark their influence on the resulting wave detection and characterization. Finally, we adapt the pipeline for the analysis of beta waves and discuss how the individual elements can be reused, rearranged, or extended to help derive analysis workflows for similar research endeavors and amplify collaborative research.Conclusions:While there are growing efforts in formalizing how neuroscientific data is represented and stored, we here present the benefits of furthermore formalizing the analysis workflows, leveraging the benefits of the diversity in data and methods towards easier collaboration and a cumulative understanding of brain function. REFERENCES[1] Adamantidis, A. R., Herrera C. G., and Gent T. C. (2019) "Oscillating circuitries in the sleeping brain." Nature Reviews Neuroscience 1-17. doi: 10.1038/s41583-019-0223-4[2] Muller, L. et al. (2018). “Cortical Travelling Waves: Mechanisms and Computational Principles.” Nature Reviews Neuroscience 19 (5): 255–68. doi: 10.1038/nrn.2018.20.[3] Sanchez-Vives, M. (2019) “Cortical activity features in transgenic mouse models of cognitive deficits (Williams Beuren Syndrome)” [Data set]. EBRAINS. doi: 10.25493/DZWT-1T8; Sanchez-Vives, M. (2019) "Cortical activity features in transgenic mouse models of cognitive deficits (Williams Beuren Syndrome)" EBRAINS. doi: 10.25493/ANF9-EG3[4] Resta, F., Allegra Mascaro, A. L., & Pavone, F. (2020) "Study of Slow Waves (SWs) propagation through wide-field calcium imaging of the right cortical hemisphere of GCaMP6f mice" EBRAINS. doi: 10.25493/3E6Y-E8G; Resta, F., Allegra Mascaro, A. L., & Pavone, F. (2021) "Study of Slow Waves (SWs) propagation through wide-field calcium imaging of the right cortical hemisphere of GCaMP6f mice (v2)" EBRAINS. doi: 10.25493/QFZK-FXS; Resta, F., [5] Allegra Mascaro, A. L., & Pavone, F. (2020) "Wide-field calcium imaging of the right cortical hemisphere of GCaMP6f mice at different anesthesia levels" EBRAINS. doi: 10.25493/XJR8-QCA[6] Brochier, T. et al. (2018) “Massively Parallel Recordings in Macaque Motor Cortex during an Instructed Delayed Reach-to-Grasp Task.” Scientific Data 5 (1): 180055. doi: 10.1038/sdata.2018.55.[7] Garcia, S. et al. (2014) “Neo: an object model for handling electrophysiology data in multiple formats.” Frontiers in Neuroinformatics 8:10. doi: 10.3389/fninf.2014.00010[8] De Bonis, G. et al. (2019) "Analysis pipeline for extracting features of cortical slow oscillations". Frontiers in Systems Neuroscience 13:70. doi: 10.3389/fnsys.2019.00070[9] Celotto, M. et al. (2020) “Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques”. Methods and Protocols 3.1:14. doi: 10.3390/mps3010014[10] Dasilva, M., et al. (2020). Modulation of cortical slow oscillations and complexity across anesthesia levels. NeuroImage, 224, 117415. doi: 10.1016/j.neuroimage.2020.117415[11] Liang, Y. (2021). “Cortex-Wide Dynamics of Intrinsic Electrical Activities: Propagating Waves and Their Interactions.” Journal of Neuroscience 41 (16): 3665–78. doi: 10.1523/JNEUROSCI.0623-20.2021

Poster

Immersive virtual environments can break the deep, everyday connection between where our senses tell us we are and where we are actually located and whom we are with. The concept of 'presence' refers to the phenomenon of behaving and feeling as if we are in the virtual world created by computer displays. In this article, we argue that presence is worthy of study by neuroscientists, and that it might aid the study of perception and consciousness.

Understanding the human brain is a "Grand Challenge" for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits.

The coronavirus pandemic considerably interrupted the educational landscape countrywide by forcing the prevalent closure of institutions from elementary to tertiary levels. With the shutting of organizations, programs which characteristically trusted on face-to-face interactions to convey content were brusquely demanded to shift to online-based education. This upheaval has procreated exclusive challenges in conveying content for various subjects and disciplines. Virtual reality technology is used to generate immersive involvements and understandings that can aid for education value and entertainment. Apart from academic and amusement, virtual reality is practically utilized in a diversity of industries, such as medicine, architecture, military, and others. This technology inspires and stimulates the students to absorb and know better in life. Strategies integrating the use of learning administration systems, web conferencing software, and opensource software can, however, accepted and recognized to alleviate the challenges in the scholarly communication, teaching and learning of information technology and other computing courses. Scholarly communication is the system and scheme through which research and other academic writings are created, assessed for excellence, dispersed to the scholarly community, and conserved for upcoming practice. Conventionally scholarly communication has occurred in the authorized literature - in journal articles, conference proceedings, book chapters and books. Some important turning point in recent years is the expansion of the Internet and the web has had a chief influence on scholarly communication. Basically the e-resources can be retrieved by research scholars through IP-filtering but it can be accessed only when they are inside the campus to overwhelmed this limitation, the INDIAN Access Management Federation (INFED) has adopted Shibboleth, open source software, for validating accredited users from institutions and offers them unified access to e-resources from anywhere, anytime. It also evades the obligation of upholding multiple passwords for multiple resources in multiple domains. This study aims to analyse the perception of Virtual Technology Tools on scholarly communication of research scholars of Alagappa University during COVID19 and the use of VT supports in research growth and progression, expert’s development and library services, (Anasi, 2018; Beemt et al., 2019; Harris and Rea, 2009). With the increase in practice of communication tools, the chances of refining efficiency of virtual technology are also growing. The widespread usage of communication tools have brought together geographically detached individuals at a very low cost., {"references":["AbdELhamid, A., Salama, A. A., Hassan, S. I., &Ayad, N. M. A. (2020, June). Towards virtual technology vision in critical cases. In: IOP Conference Series: Materials Science and Engineering (Vol. 870, No. 1, p. 012134). IOP Publishing. 2. Bailenson, J. (2018). Protecting nonverbal data tracked in virtual reality. JAMA pediatrics, 172(10), 905-906. 3. Becker, M. C., Salvatore, P., & Zirpoli, F. (2005). The impact of virtual simulation tools on problem-solving and new product development organization. Research policy, 34(9), 1305-1321. 4. Bolkas, D., Chiampi, J., Chapman, J., Fioti, J., & Pavill IV, V. F. (2020).Creating immersive and interactive surveying laboratories in virtual reality: A differential leveling example. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5, 9-15. 5. Bouchard, S., St-Jacques, J., Robillard, G., & Renaud, P. (2008). Anxiety increases the feeling of presence in virtual reality. Presence: Teleoperators and Virtual Environments, 17(4), 376-391. 6. Boyd, D. E., & Koles, B. (2019). Virtual reality and its impact on B2B marketing: A value-in-use perspective. Journal of Business Research, 100, 590-598. 7. Chanishvili, K.(2020) Covid19 Pandemics, Distance Teaching and development of Critical Thinking through Moodle Platform. 8. De Gauquier, L., Brengman, M., Willems, K., & Van Kerrebroeck, H. (2019). Leveraging advertising to a higher dimension: experimental research on the impact of virtual reality on brand personality impressions. Virtual Reality, 23(3), 235-253. 9. Diemer, J., Alpers, G. W., Peperkorn, H. M., Shiban, Y., & Mühlberger, A. (2015). The impact of perception and presence on emotional reactions: a review of research in virtual reality. Frontiers in psychology, 6, 26. 10. Dominguez, D., Bowser, G., Whipple, S., Dominguez Vasquez, C., & Rohlf, A. (2020, December). Pollinator Hotshots: A framework for Virtual Learning and Field Experiences during the Coronavirus Pandemic. In: AGU Fall Meeting Abstracts (Vol. 2020, pp. ED051-0001). 11. Flavián, C., Ibanez-Sanchez, S., & Orús, C. (2019). The impact of virtual, augmented and mixed reality technologies on the customer experience. Journal of business research, 100, 547-560. 12. Kellmeyer, P., Biller-Andorno, N., & Meynen, G. (2019). Ethical tensions of virtual reality treatment in vulnerable patients. Nature medicine, 25(8), 1185-1188. 13. Kenyon, R. V., Leigh, J., &Keshner, E. A. (2004).Considerations for the future development of virtual technology as a rehabilitation tool. Journal of neuroengineering and rehabilitation, 1(1), 1-10. 14. Olson, J. D., Appunn, F. D., McAllister, C. A., Walters, K. K., & Grinnell, L. (2014). Webcams and virtual teams: An impact model. Team Performance Management. 123 4th I-LISS International Conference - IIC 2021 15. Orlikowski, W. J., & Barley, S. R. (2001). Technology and institutions: What can research on information technology and research on organizations learn from each other?. MIS quarterly, 145-165. 16. Peperkorn, H. M., Alpers, G. W., & Mühlberger, A. (2014). Triggers of fear: perceptual cues versus conceptual information in spider phobia. Journal of clinical psychology, 70(7), 704-714. 17. Rahimi, N., & Martin, N. L. (2020). Challenges and strategies for online teaching in information technology and other computing programs. In Proceedings of the 21st Annual Conference on Information Technology Education (pp. 218-1222). 18. Sanchez-Vives, M. V., & Slater, M. (2005). From presence to consciousness through virtual reality. Nature Reviews Neuroscience, 6(4), 332-339. 19. Schön, D. (1983). The reflective practitioner basic books. New York. 20. Seth, A. K., Suzuki, K., & Critchley, H. D. (2012). An interoceptive predictive coding model of conscious presence. Frontiers in psychology, 2, 395. 21. Singh, R. P., Javaid, M., Kataria, R., Tyagi, M., Haleem, A., & Suman, R. (2020). Significant applications of virtual reality for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 661-664. 22. Violante, M. G., Vezzetti, E., & Piazzolla, P. (2019). Interactive virtual technologies in engineering education: Why not 360° videos?. International Journal on Interactive Design and Manufacturing, 13(2), 729-742."]}

Making progress in neuroscience is increasingly a collaborative effort that requires insights obtained across data, methodologies, and models. Individual puzzle pieces must eventually be properly related to one another and integrated to frame the research in a coherent picture. In this process, often three questions arise: How to make results comparable? How to relate complementary, yet different methodological approaches to one another? How to incorporate and validate models based on these findings? A prominent example of such a scenario is slow cortical waves which are persistently observed in sleeping and anesthetized subjects of various species and across various measurement techniques [1,2], as well as being expressed by various models. While slow waves are a ubiquitous phenomenon with a rich literature basis and have been proposed to be the default activity pattern of the cortical network [3], the diversity of the experimental data and the numerous different analytical methods, tools, and even terminologies makes it difficult to rigorously compare and interrelate results from the various sources to form a coherent understanding. Here, we present an analysis pipeline for the study of slow wave activity to support quantitative comparisons of analysis results across data sources and algorithms. A key concept of the pipeline is modularity at the correct level of granularity in order to flexibly adapt, reuse, and extend it to a wide range of datasets and research questions. In the spirit of reproducibility, individual analysis blocks are built on a number of community-driven, open-source software tools, e.g., the workflow manager Snakemake (RRID:SCR_003475), the Neo (RRID:SCR_000634) library for data representation [4], the Elephant (RRID:SCR_003833) analysis toolbox, and the pipeline execution can be captured in the EBRAINS Knowledge Graph (https://kg.ebrains.eu). This pipeline design enables multi-scale analyses of measured slow wave activity, which we demonstrate using ECoG [5,6,7] and Calcium Imaging [8,9] data of anesthetized mice. Furthermore, the integration of model data provides a basis for rigorous validation testing [10,11]. While the ���same method - different data��� approach enables fair comparisons, the pipeline equally enables ���same data - different methods��� benchmarking. Finally, we discuss how the re-usable and adaptable conceptual design helps to derive analysis pipelines for similar research endeavors to amplify collaborative research. Acknowledgements This project has received funding from the European Union���s Horizon 2020 Framework Programme for Research and Innovation, Specific Grant Agreement No. 785907 (Human Brain Project SGA2) and 945539 (Human Brain Project SGA3), and by the Helmholtz Association Initiative and Networking Fund ZT-I-0003. References Adamantidis, A. R., Herrera C. G., and Gent T. C. (2019) "Oscillating circuitries in the sleeping brain." Nature Reviews Neuroscience 1-17., 10.1038/s41583-019-0223-4 Alkire, M. T., Hudetz A. G. , and Tononi G. (2008) "Consciousness and anesthesia." Science 322.5903: 876-880., 10.1126/science.1149213 Sanchez-Vives, M. V., Massimini, M., and Mattia, M. (2017) ���Shaping the default activity pattern of the cortical network���. Neuron, 94(5), 993-1001., 10.1016/j.neuron.2017.05.015 Garcia, S., Guarino, D., Jaillet, F., Jennings, T., Pr��pper, R., Rautenberg, P.L., Rodgers, C.C., Sobolev, A., Wachtler, T., Yger, P., et al. (2014). Neo: an object model for handling electrophysiology data in multiple formats. Frontiers in Neuroinformatics 8, 10., 10.3389/fninf.2014.00010 Dasilva et al. (2020) ���Altered Neocortical Dynamics in a Mouse Model of Williams���Beuren Syndrome���. Molecular Neurobiology, vol. 57, no 2, p. 765-777., 10.1007/s12035-019-01732-4 Sanchez-Vives, M. (2019) ���Cortical activity features in transgenic mouse models of cognitive deficits (Williams Beuren Syndrome)��� [Data set]. EBRAINS., 10.25493/DZWT-1T8 De Bonis, G. et al. (2019) "Analysis pipeline for extracting features of cortical slow oscillations". Frontiers in Systems Neuroscience 13:70., 10.3389/fnsys.2019.00070 Resta, F., Allegra Mascaro, A. L., and Pavone, F. (2020). Study of Slow Waves (SWs) propagation through wide-field calcium imaging of the right cortical hemisphere of GCaMP6f mice [Data set]. EBRAINS., 10.25493/3E6Y-E8G Celotto, M. et al. (2020) ���Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques���. Methods and Protocols 3.1:14., 10.3390/mps3010014 Gutzen, R. et al. (2018) "Reproducible neural network simulations: statistical methods for model validation on the level of network activity data." Frontiers in Neuroinformatics 12:90., 10.3389/fninf.2018.00090 Trensch, G. et al. (2018) "Rigorous neural network simulations: a model substantiation methodology for increasing the correctness of simulation results in the absence of experimental validation data." Frontiers in Neuroinformatics 12:81., 10.3389/fninf.2018.00090, Also funded by: Helmholtz Association Initiative and Networking Fund ZT-I-0003.

This paper studies the aspect of presence in a Virtual Reality (VR) environment that can be used for training purposes in the education sector and more specifically for teacher training and professional development. During the VR experience trainees had the chance to view the world from different perspectives through the eyes of different characters appearing in the scene. The experimental evaluation conducted aims to examine the effect of viewing the experience from different perspectives and viewpoints in relation to the overall user experience and the level of presence achieved. To accomplish these objectives an experiment was performed investigating presence and the correlation between presence and different viewpoints/perspectives. To measure presence a combination of methods were used including two different questionnaires, the use of an eeg device, EMOTIV EPOC+ and the analysis of heart rates. The results indicate that high levels of presence were recorded and that increased levels of presence are associated with viewing the VE from a student rather than a teacher perspective., This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development. DOI 10.2312/egve.20181329, Studying Levels of Presence in a Virtual Environment Simulating Drug Use in Schools: Effect on Different Character Perspectives, Christofi, M., Baka, E., Stavroulia, K.E., Michael-Grigoriou, D., Lanitis, A. & Magnenat-Thalmann, ICAT-EGVE 2018 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments, Bruder, Gerd and Yoshimoto, Shunsuke and Cobb, Sue, 2018, pages 163-170, Copyright ©2018 The Eurographics Association, The Eurographics Association., {"references":["BATSON C. D.: These things called empathy: eight related but distinct phenomena.","BOUCHARD S.: Emotions and the emotional valence afforded by the virtual environment. In Virtual reality. InTech, 2010.","BAKA E., STAVROULIA K. E., MAGNENAT-THALMANN N., LANITIS A.: An eeg-based evaluation for comparing the sense of presence between virtual and physical environments. In Proceedings of Computer Graphics International 2018 (2018), ACM, pp. 107–116.","HEALTH M., CENTERS W.: The bloodshot look: Why cannabis causes red eyes, Oct 2016. URL: http://www.medwellhealth.net/ the-bloodshot-look-why-cannabis-causes-red-eyes/.","HEETER C.: Being there: The subjective experience of presence. Presence: Teleoperators & Virtual Environments 1, 2 (1992), 262–271.","HODGES L. F., KOOPER R., MEYER T. C., DE GRAAFF J. J. H., ROTHBAUM B. O., OPDYKE D., WILLIFORD J. S., NORTH M. M.: Presence as the defining factor in a VR application. Tech. rep., Georgia Institute of Technology, 1994.","KOBER S. E., KURZMANN J., NEUPER C.: Cortical correlate of spatial presence in 2d and 3d interactive virtual reality: an eeg study. International Journal of Psychophysiology 83, 3 (2012), 365–374.","KOUNNOU A.: \"they gave substance to my 12-year-old son at school\" complains father, Apr 2017. URL: http://www. reporter.com.cy/editors-choice/article/187411/.","LOMBARD M., DITTON T.: At the heart of it all: The concept of presence. Journal of Computer-Mediated Communication 3, 2 (1997).","MEEHAN M., INSKO B., WHITTON M., BROOKS F.: An objective surrogate for presence: Physiological response. In 3rd International Workshop on Presence (2000).","ON DRUG ABUSE N. I.: What are marijuana effects?, Feb 2018. URL: https://www.drugabuse. gov/publications/research-reports/marijuana/ what-are-marijuana-effects.","ROGERS S.: Why is presence important for virtual reality?, Nov 2017. URL: https://www.vrfocus.com/2017/11/ why-is-presence-important-for-virtual-reality/.","REGENBRECHT H. T., SCHUBERT T. W., FRIEDMANN F.: Measuring the sense of presence and its relations to fear of heights in virtual environments. International Journal of Human-Computer Interaction 10, 3 (1998), 233–249.","STAVROULIA K. E., BAKA E., CHRISTOFI M., MICHAELGRIGORIOU D., MAGNENAT-THALMANN N., LANITIS A.: A virtual reality environment simulations drug use in schools: effect on emotions and mood states. In In The International Conference on Information Communication Technologies in Education (ICICTE 2018) Proceedings (2018), pp. 225–234.","SHERIDAN T. B.: Further musings on the psychophysics of presence. Presence: Teleoperators & Virtual Environments 5, 2 (1996), 241– 246.","SZILY E., KÉRI S.: Emotion-related brain regions. Ideggyógyászati szemle 61, 3-4 (2008), 77–86.","SLATER M., SANCHEZ-VIVES M. V.: Enhancing our lives with immersive virtual reality. Frontiers in Robotics and AI 3 (2016), 74.","SLATER M., USOH M.: Representations systems, perceptual position, and presence in immersive virtual environments. Presence: Teleoperators & Virtual Environments 2, 3 (1993), 221–233.","SCHUEMIE M. J., VAN DER MAST C.: Presence: Interacting in virtual reality. In Proceedings, Twente Workshop on Language Technology (1999), vol. 15, pp. 213–217.","SCHUEMIE M. J., VAN DER STRAATEN P., KRIJN M., VAN DER MAST C. A.: Research on presence in virtual reality: A survey. CyberPsychology & Behavior 4, 2 (2001), 183–201.","THOMAS G., KLONER R. A., REZKALLA S.: Adverse cardiovascular, cerebrovascular, and peripheral vascular effects of marijuana inhalation: what cardiologists need to know. The American journal of cardiology 113, 1 (2014), 187–190.","USOH M., CATENA E., ARMAN S., SLATER M.: Using presence questionnaires in reality. Presence: Teleoperators & Virtual Environments 9, 5 (2000), 497–503.","VÄSTFJÄLL D.: The subjective sense of presence, emotion recognition, and experienced emotions in auditory virtual environments. CyberPsychology & Behavior 6, 2 (2003), 181–188.","WATSON D., CLARK L. A., TELLEGEN A.: Development and validation of brief measures of positive and negative affect: the panas scales. Journal of personality and social psychology 54, 6 (1988), 1063.","WITMER B. G., SINGER M. J.: Measuring presence in virtual environments: A presence questionnaire. Presence 7, 3 (1998), 225–240."]}