Summary
Disorders of consciousness (DoC) after severe brain injury are marked by impairments in arousal and/or awareness and affect millions of people worldwide. Advanced functional neuroimaging and electrophysiological (AIE) techniques have provided several new tools for improving both the diagnosis and prognosis in patients with DoC. This tutorial will introduce participants to recent advancements in AIE techniques, with an emphasis on data analysis, translating scientific approaches to clinic practices, and ethical/practical considerations. In this interactive tutorial, leaders from various disciplines will outline how multimodal AIE techniques can detect preserved consciousness and predict chances of recovery on an individualized patient basis. Using a case-based learning approach, we will provide a comprehensive overview of neuroimaging in DoC, the different technical approaches employed (i.e. fMRI, EEG, TMS-EEG, fNIRS, PET), the imaging paradigms used (active, passive, resting state) and the types of inferences that can be made based on those paradigms (e.g., perception, awareness, communication). Next, we will provide a hands-on tutorial on advancements in analysis techniques through interactive demonstrations on how to use and interpret publicly accessible pipelines for analyzing single-subject DoC data. Participants will observe a live TMS-EEG session, providing exposure to these techniques and an interactive discussion of the advantages and limitations of using AIEs with DoC patients. Finally, we will outline barriers in clinical translation (and discuss how these barriers might be overcome), and outline which patients stand to benefit the most from these neuroimaging techniques and consider when during their clinical trajectory imaging tests are likely to be most useful.
Rationale on speaker selection and proof of their expertise
This tutorial brings together a group of international experts throughout various career stages with a wide variety of expertise in different analytical methods for assessing brain function and behavioural responses in DoC patients. Dr. Karnig Kazazian, a junior research scientist at Western University, specializes in fMRI and fNIRS applications in clinical settings, co-leads the DoC neuroimaging program in London, Ontario, has advanced the use of fNIRS in DoC research and has published multiple peer-reviewed papers in DoC research. Dr. Aurore Thibaut is an internationally renowned expert in DoC research, the co-director of the Coma Science Group and an associate professor at the University of Liège, has published several seminal papers in DoC research, thereby establishing herself as a pioneer and world leader in imaging, behavioural assessments and therapeutics in DoC patients. Dr. Mario Rosanova is an associate professor at the University of Milan and a world expert in TMS-EEG. He has made significant contributions by applying these techniques to study brain activity in DoC, with multiple seminal contributions to the field of DoC and more broadly consciousness research as a whole. Dr. Marzia De Lucia is a scientist at Lausanne University hospital and combines machine learning with EEG to study DoC. Dr. De Lucia has pioneered novel analysis techniques to design prognostic models in DoC, has published several high impact publications and delivered numerous talks on using EEG with DoC patients. Together, these speakers provide a comprehensive and innovative perspective on AIE methods in DoC.
Desired educational expectations
This tutorial is designed to meet participants’ educational expectations by offering a structured, interactive learning experience across three sections. The introductory session will provide an educational foundation in the latest DoC research, covering neuroimaging and electrophysiological assessments and introducing paradigms like resting-state, passive, and active task-based approaches. Case-based demonstrations will allow participants to practically understand these paradigms, promoting an understanding of how different techniques apply to individual patient needs. Moving into practical application, participants will engage in hands-on demonstrations of data analysis techniques for EEG, PET, and fNIRS, gaining exposure to advanced machine learning methods and innovative analytical approaches relevant to DoC. Access to real data, code, and repositories will enable participants to follow along and practice, with structured guidance to reinforce their learning. Breakout groups will cater to specific interests, such as preferred imaging techniques, allowing for a tailored learning experience. A live TMS-EEG demonstration will provide direct observation of data collection and interpretation, ensuring that participants leave with a comprehensive and practical understanding of AIE applications in DoC. Finally, participants will gain insight into the barriers to clinical translation of advanced neuroimaging techniques in DoC and strategies to overcome them. They will also learn which patient populations benefit most from these techniques and when imaging tests are most useful during a patient's clinical trajectory.
Proposed audience engagement
We will engage the audience by combining interactive discussions, hands-on activities, and case-based learning throughout the tutorial. In the introductory session, participants will actively explore the clinical relevance of various consciousness assessment techniques through real-world case examples, promoting engagement with group polling software (such as Menti) and critical thinking by posing questions and scenerios to the audience which they can answer with anonymous polling. The hands-on demonstration of data analysis techniques for EEG, PET, and fNIRS will allow participants to work with actual data, fostering a deeper understanding of these methods. Interactive elements, such as breakout discussions with a tutorial leader will allow participants to tailor their learning to there preferred imaging techniques, which will also encourages networking with like-minded colleagues. Additionally, the live TMS-EEG demonstration will provide a dynamic opportunity for participants to observe data collection and analysis in real time, further enhancing their engagement and practical knowledge. Throughout the entire tutorial, questions and discussion prompts will be routinely provided to the audience to ensure engagement and interactive discussions.
Planned structure
This tutorial is organized into three sections, each blending theory with hands-on engagement to deepen understanding of AIE techniques in DoC. The introductory session will provide an overview of recent advancements in DoC research, focusing on neuroimaging and electrophysiological assessments and covering consciousness assessment paradigms such as resting-state, passive, and active task-based approaches. Through case-based demonstrations, we will illustrate how these paradigms are applied, allowing participants to explore the clinical relevance of each technique. A case-based approach will further guide discussion on selecting the most appropriate techniques and paradigms for individual patients, highlighting the importance of personalized assessment in DoC. Next, we will conduct a hands-on demonstration of various data analysis techniques, showcasing how EEG, PET, and fNIRS data can be effectively analyzed in the context of DoC. We will also discuss advancements in machine learning and cutting-edge analytical approaches used in this field. Participants will have access to data, code, and repositories for this session and will be guided step-by-step through the process, providing a practical and interactive experience to deepen their understanding. The session will be further enhanced using polls and time to break out into smaller groups with the tutorial leaders based on preference of imaging techniques. We will walk participants through interpreting EEG, PET, and BCI data, with hands-on guides allowing attendees to experiment with data processing and independently generate consciousness indices Finally, the hand-on demonstration offers a live TMS-EEG demonstration where participants can observe data collection and analysis firsthand.
Rationale on panel inclusivity
This tutorial honors diversity by thoughtfully selecting a panel inclusive of various scientific backgrounds, career stages, gender expressions, national origins, ethnicities, and LGBTQ+ representation. The speakers are balanced in gender, with two men and two women, and bring a range of career stages, from junior to senior researchers, fostering a breadth of perspectives. Geographic and ethnic diversity are also well-represented, with researchers from different regions and backgrounds. Dr. Karnig Kazazian, a junior researcher from Canada, brings an LGBTQ+ perspective and a Middle Eastern ethnic background, adding valuable insight into underrepresented communities in science. Dr. Aurore Thibaut, a senior female researcher, contributes deep expertise alongside Dr. Mario Rosanova, a senior male researcher, both offering advanced knowledge in the field. Dr. Marzia De Lucia, a mid-career researcher, bridges early-career and established expertise, providing a nuanced viewpoint on DoC. This carefully balanced panel celebrates diversity in all aspects, fostering an inclusive and comprehensive learning experience for all participants.
