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INCF Neuroinformatics Assembly 2023 - Sessions by topic

Please find below the sessions that are conducted during INCF Neuroinformatics Assembly 2023. Please click on the title to expand and see the details.

AI for Neurodegenerative Diseases

  • AI
  • deep learning
  • machine learning
icon keynoteChair: Mah Parsa

About two percent of the world's population suffers from various types of mental health disorders. Furthermore, more than 50 million people worldwide are living with different types of neurodegenerative dementias including Alzheimer’s disease (AD). Early detection of mental health disorders and dementia would help patients seek out different intervention programs, as well as clinical interventions so that they can maintain their quality of life at the normal level. 

The size of the above problems and their global effect requires international and multidisciplinary collaborations that address the needs of underserved patient populations without access to costly care in the hands of sparsely available specialists such as psychiatrists. To fill these gaps in a cost-effective and scalable manner, it is critical to foster research partnerships that result in the development of Artificial Intelligence (AI)-driven solutions to measure, diagnose, and treat mental and neurodegenerative disorders. 

Goals:

We aim to discuss ongoing projects for: 

  • Signal processing for biosignals related to mental disorders and  neurodegenerative diseases
  • Developing machine learning (ML) and deep learning (DL) based systems in doing early detection of mental disorders and neurodegenerative diseases 
  • Developing AI approaches adopting the principles of ethics in mental disorders and neurodegenerative diseases 
  • Developing explainable-AI approaches for prognosis of mental disorders and neurodegenerative diseases 
  • Developing AI approaches by adapting transfer learning strategies for the diagnose, prognosis and treatment of mental disorders and neurodegenerative diseases 

By providing a comprehensive discussion on the above topics, the workshop will allow mental health professionals such as psychologists, psychiatrists and mental health counselors and researchers in the field of mental disorders and neurodegenerative diseases expand their knowledge regarding ongoing AI research projects and innovative AI applications. It also offers breathtaking advantages for the AI community to improve their apps by receiving valuable real-world feedback from clinical audiences.

Expected outcomes:

We expect to strengthen the collaboration between scientists and researchers from the computer science domain, in particular ML researchers in the field of speech, natural language and image processing and neurologists and mental health professionals. We also intend to motivate industry labs to financially and technically support research projects related to mental disorders and neurodegenerative diseases. 

We assume holding the workshop can encourage principal investigators at universities to currently employ services for the early diagnosis, treatment, and management of mental disorders and neurodegenerative diseases 

Additionally, the workshop will:

  • Enhance mental health experts' knowledge with learning about state-of-the-art ML tools;
  • Provide valuable feedbacks for AI experts from neurologists and mental health professionals;
  • Fill the gap between neurologists and mental health professionals and ML practitioners;
  • Make a bridge between industrial researchers, academic research scientists, and young AI entrepreneurs working in the field of mental disorders and neurodegenerative diseases 
Target audience:

International neurologists and mental health professionals, neuroscientists, psychiatrists, and computer scientists (in particular ML scientists and data scientists) are the main groups of the workshop's audience. Attendance of the workshop will provide them a unique chance to start multidisciplinary, international cutting-edge research collaborations that could lead to original and innovative AI-based solutions for mental disorders and neurodegenerative diseases. Students and trainees from different disciplines can learn about advanced tools by attending this workshop.

Schedule:

We will endeavor to organize a one-day interactive workshop that will be started by keynote speakers to present: 

Challenges in diagnosing and/or quantitatively detecting neurodegenerative diseases and Alzheimer’s. and ML-based diagnostic/measurement systems that can mitigate these challenges.

The workshop will include lightning talks on the topic of analysis of neuroimaging datasets for neurodegenerative diseases and Alzheimer’s, as well as a description of newly developed AI systems and ML algorithms that could be useful to analyze neuroimaging datasets.

One of the exciting parts of our workshop is group discussions to review how the state-of-the-art AI systems and ML algorithms can be utilized for:

  1. Early detection, diagnosis, measurement, and treatment of mental and neurodegenerative disorders;

  2. Ongoing support of people living with neurodegenerative diseases and Alzheimer’s;

  3. Efficient processing of large amounts of clinical data related to neurodegenerative diseases and Alzheimer’s;

  4. Detecting racism from texts, speech, and its effect on individuals' mental health (we aim to describe AI community efforts to develop AI systems to address mental health disparities);

  5. Fairly diagnosing neurodegenerative diseases and Alzheimer’s.

We will have two panel discussions: 1) an academy panel discussion and 2) an industry panel discussion to discuss the following questions: 

  1. What are some of the different ways AI can be used for neurodegenerative diseases such as Alzheimer’s ? (including, , prediction, diagnostics and prognostics)

  2. How can we develop AI applications for  neurodegenerative diseases such as Alzheimer’s when there is not enough evidence-based information or peer-reviewed science? 

  3. How did you manage to get access to neurodegenerative diseases such as Alzheimer’s datasets?

  4. What is your approach to ensuring fairness in ML-based assessment tools for dementia and Alzheimer’s?

  5. What are the practical barriers to the implementation of AI for neurodegenerative diseases such as Alzheimer’s?

  6. How can investors and other market participants assess the feasibility, viability, and desirability of mental healthcare-focused AI products?

The two panel discussions will aim to fill the gap between neurologists and senior researchers in mental healthcare and industrial leaders in AI. Also, to discuss AI entrepreneurs can effectively collaborate with psychiatrists, neurologists to use AI to detect neurodegenerative diseases. We also aim for such panels to encourage students in psychiatry, psychology, neuroscience and computer scientists, and share their innovative ideas with entrepreneurs to develop amazing AI systems to detect neurodegenerative diseases such as AD.

In addition to the sessions mentioned above, we will offer two poster sessions during lunch and coffee breaks. Finally, we will wrap up the workshop by announcing awards for the “best” lightning talk and poster presentation and the “best” reviewer.

 

FAIR data: The role of journals

  • policies
  • publications
  • data repositories
  • data sharing
  • open data
icon keynoteChair: Jan Bjaalie

Most neuroscience journals request authors to make their data publicly available in appropriate repositories. The requirements and policies put forward by journals vary, and the services provided for different types of data also differ considerably across repositories. Researchers trying to navigate to a suitable repository are challenged with several questions and a need to compromise parameters such as storage space available, time and efforts needed for data sharing, data governance considerations et cetera, all influencing the extent of FAIRness their data may achieve. Many journals provide data repository guidance, but with many options and priorities available, data tend to be dispersed across many repositories. In this session we discuss how journal policies and recommendations contribute to open and FAIR neuroscience and influence the findability and interoperability of public data.

Topics:
  • How journal policies and recommendations contribute to open and FAIR neuroscience
  • Findability and interoperability of public data

Neuroscience data integration through use of digital brain atlases

CREDIT COURSE - Level: PhD, Credits: 2 ECTS

  • brain atlas
  • data integration
  • data sharing
icon keynoteTrygve Brauns Leergaard, Ingvild Elise Bjerke - University of Oslo

Understanding how the brain works is one of the grand challenges in science and requires the integration of huge amounts of heterogeneous and complex data. Numerous research publications present experimental data at various levels of granularity and describe a wide range of structural and functional aspects of the brain. The management of this deluge of data represents a bottleneck for progress, with a main challenge being that the multiple data categories are difficult to compare. In this context, reference atlases of the brain are important tools for assigning anatomical location and (semi-)automatically analyzing data captured with the many methods and instruments used to study the brain. Reference atlases for the brain rank among the most frequently used and highest cited publications in neuroscience. But integration of data through the use of conventional reference atlases has been difficult to achieve.

With a new generation of three-dimensional digital reference atlases, new solutions for integrating and disseminating brain data are being developed. In many ways, future digital reference atlases and the data systems that will be built around them will be similar to current geographical atlases, such as Google Maps and Google Earth, which provide interactive access to huge amounts of high resolution image data, together with additional information (annotations, practical information, photographs) and more detailed visualizations (e.g. "street view") for specific areas. Digital brain atlases play an important role in several large international projects, including the European Union ICT Future Emerging Technologies Flagship project, the Human Brain Project.This course contains an introduction to currently available reference atlases for mouse and rat brain. It will demonstrate how the 3D brain templates for the reference atlases are acquired, how they are used as a basis for delineating the structures of the brain, how they can be enriched by other data modalities, and how they can be used as a basis for assigning location (coordinate based or semantic) to a wide range of structural and functional data collected from the brain. The course will also outline examples of data system employed to organize neuroscience data collections in the context of reference atlases as well as analytical workflows applied to the data, with opportunities for hands-on exploration of selected tools.

Read more about how digital brain atlases are used in the EBRAINS infrastructure at https://ebrains.eu/services/atlases

Registration

  • The course is open for up to 40 participants.

Schedule
The course will be arranged as a digital Satellite event to the 2023 INCF Neuroinformatics assembly.

Practical information about attendance via Zoom, program and syllabus / reading list will be published on this page. Examination will be in the form of a home assignment, to be completed within two days after the course (estimate work time = 8 hours).

Streamlining Cross-Platform Data Integration: Processes and Solutions for Rapidly Developing an Integrated Workflow Across Independent Systems for the US BRAIN Initiative Cell Census

  • FAIR workflows
  • dataintegration
  • infrastructures
icon keynoteChairs: Bing-Xing Huo, Patrick Ray, Maryann Martone

Advances in neuroscience increasingly require researchers and practitioners to work with data from multiple sources to gain insights into brain organization, function and disease. Many neuroscience data platforms have made these large, complex datasets available to the community. Integrating data from these diverse sources has been a challenging task as each platform is developed independently. At the same time, the infrastructure developed through large investments by the national-level brain projects is expected to be leveraged by new projects which may have custom workflows. Developing cross-platform data integration processes and solutions is essential therefore both for enabling integrated workflow from independent systems in a short time frame and gaining insights from their data.

This workshop is designed as a forum for neuroscientists, data scientists, and software engineers to come together and discuss the challenges, lessons, strategies and best practices of cross-platform neuroscience data integration. Participants will also share and communicate on software tools and infrastructure solutions that support efficient data integration, such as data management systems, data integration platforms, cloud computing, and APIs.

The workshop will cover topics at ecosystem level such as standardization, governance, and success metrics drawing from experiences in the US BRAIN Initiative Cell Census Network (BICCN) and its next phase, the BRAIN Initiative Cell Atlas Network (BICAN), with a focus on streamlining integrated workflows across independent systems. The challenge in both these projects was to quickly develop an end-to-end system to support the specific requirements of these consortia from systems that were developed independently. Projects like these present the ultimate use cases for FAIR, as FAIR practices should greatly reduce the barrier to integration and increase the usability of the data.

Objective:

Through a combination of presentations, panel discussions, and interactive sessions, participants will engage in an in-depth discussion on the principles of cross-platform data integration in neuroscience and learn how to apply these principles to their own platforms and research projects. Participants will also learn about open-source software tools and infrastructure solutions that support open data and open science. By the end of the workshop, participants will have increased awareness and alignment on the strategies and challenges of cross-platform data integration processes, better communication channels across independent systems, and also the skills and knowledge needed to develop efficient and effective cross-platform data integration.

Target audience:

The target audience for this workshop would likely be professionals working at the intersection of neuroscience and data science, including researchers, IT personnel, and bioinformaticians. The workshop would be beneficial for those who are involved in or responsible for the integration of data from independent systems and are interested in learning about processes and solutions for streamlining this process. Attendees may be at different stages of their careers, and may come from academic, industry, or government backgrounds.

Infrastructure for Sensitive Data

  • Infrastructure
  • sesitive data
  • computational neuroscience
  • GDPR
icon keynoteChair: Petra Ritter

Regulation of personal data - such as medical brain data, has been radically transformed in recent years with the introduction of several new legislation, including the European GDPR (General Data Protection Regulation). This means an upheaval of prior work routines with the regard to collecting, processing and storing, as well as sharing personal data. These new regulations seek to establish a balance between safeguarding the data protection rights of citizens, while enabling innovative uses of research tools at the same time. We present the new EBRAINS privacy-compliant and scalable services for brain research to store, share and analyze complex multi-modal FAIR sensitive neuroscience data originating from human subjects: Health Data Cloud (HDC). The workshop provides participants both lecture and hands-on training session.

Audience:

The target audience for this workshop would likely be professionals working at the intersection of neuroscience and data science, including researchers, IT personnel, and bioinformaticians. The workshop would be beneficial for those who are involved in or responsible for the integration of data from independent systems and are interested in learning about processes and solutions for streamlining this process. Attendees may be at different stages of their careers, and may come from academic, industry, or government backgrounds.

Topics:
  • Get introduced to GDPR-compliant governance and data protection framework, as well as FAIR data principles and open standards
  • Discover the new GDPR-compliant Health Data Cloud (HDC) ecosystem
  • Get introduced to Data structure standards (BIDS)
  • Discover the BIDS computational neuroscience data format
  • Get introduced to Knowledge-Graphs and Ontologies
  • Discover The Virtual Brain Ontology (TVB-O) & The Virtual Brain Adapter of Semantics (TVBase)
Agenda:

9-10 Legal frameworks for sharing sensitive data at different legislations (Petra Ritter)
10-11 Introduction to Secure Virtual Research Environments (Petra Ritter)
11-12 Technical measures to keep data and information safe (Michael Schirner)
12-13 Organizational measures to keep data and information safe (Michael Schirner)
13-14 Lunch Break
14-15 Hands-on: Onboarding an Infrastructure for Sensitive data (Patrik Bey)
15-16 Hands-on: Processing sensitive data securely and reproducible (Marc Sacks)
16-17 Hands-on: Sharing sensitive data (Jil Meier)

We are seeking speakers for this investigator-led session at INCF Neuroinformatics Assembly 2023. This session will take place on Mon Sep 18 from 14:30-16:00 CEST. There 4 speaker slots available, and each speaker will be given 15 mins for their talk plus a 5 min Q&A, for a total of 20 mins per speaker. After all 4 speakers have presented, there will be a roundtable discussion for the remainder of the timeslot.
Submit your abstract here.
This is a participant-led session
We are seeking speakers for this investigator-led session at INCF Neuroinformatics Assembly 2023. This session will take place on Mon Sep 18 from 19:00-20:30 CEST. There 4 speaker slots available, and each speaker will be given 15 mins for their talk plus a 5 min Q&A, for a total of 20 mins per speaker. After all 4 speakers have presented, there will be a roundtable discussion for the remainder of the timeslot.
Submit your abstract here.
This is a participant-led session

Practical guide to overcome the reproducibility crisis in small animal neuroimaging: workflows, tools, and repositories

  • MRI
  • data aqusition
  • data sharing
  • data analysis
icon keynoteCo-Chairs: Markus Aswendt, Joanes Grandjean

Small animal MRI has undergone rapid development over the past decade in terms of improved hardware and acquisition protocols. Similar to human MRI, it is now possible to acquire whole brain functional and structural connectomes in a variety of disease models. Recent large-scale multicenter studies have shown that there is a non-negligible variability in MRI setups, animal protocols, and post-processing methods. Further, only a minority of labs actively apply standardized quality control, analysis tools, and FAIR data sharing.

Audience:

PhD students, Postdocs, (PIs). Expertise in programming not necessary. Prior experience with rodent MRI data acquisition and processing is a plus.

Methods:

The workshop will include interactive seminars given by selected experts in the field covering all aspects of (FAIR) small animal MRI data acquisition, analysis, and sharing. The seminars will be followed by hands-on training where participants will perform use case scenarios using software established by the organizers. This will include an introduction to the basics of using command line interfaces, Python installation, working with Docker/Singularity containers, Datalad/Git, and BIDS.

Schedule:
  • SEMINAR 1: Introduction (20 min)
    How to set up a FAIR workflow for multimodal rodent imaging data in a no-code environment of a translational stroke lab + Overview of the workshop
  • SEMINAR 2: Basics (40 min)
    What you should know about the command line, anaconda/pip!
    How to apply version control for code and data using Git, GitHub, and Datalad
    What are Docker/Singularity containers? Principles and benefits of BIDS.
  • HANDS-ON (1 h)
    Install Git, Datalad, Singularity/Docker and transfer your data to an online repository
  • SEMINAR 3 and HANDS-ON (2 hrs)
    Application of image post-processing pipelines RABIES or AIDAmri

Event annotation in neuroimaging using HED: from experiment to analysis

  • HED
  • BIDS
icon keynoteChair: Scott Makeig

Hierarchical Event Descriptors (HED) fill a major gap in the neuroinformatics standards toolkit, namely the specification of the nature(s) of events and time-limited conditions recorded as having occurred during time series recordings (EEG, MEG, iEEG, fMRI, etc.). Without standardized annotation of dataset events and conditions, neuroimaging researchers cannot re- use shared datasets to perform most standard or advanced analyses. Relating recorded brain dynamics to stimuli, experience and behavior requires extensive manual coding based on dataset- specific information gleaned from data authors’ notes or publications. Standardized annotation of temporal structure and events is therefore fundamental for new analyses exploiting new contrasts and commonalities across datasets and study designs. HED provides an easy-to-use machine-actionable framework for providing this annotation within BIDS-formatted datasets. In the last two years, the HED Working Group has made major advances in the structure, syntax, and tools for HED annotation of time series data saved in the BIDS format. These advances have made HED tagging of BIDS compatible datasets ready for widespread use and are documented at https://www.hed-resources.org. We, the HED Working Group, propose a half-day online INCF workshop on the need for, structure of, tools for, and use of HED annotation to prepare neuroimaging time series data for storing, sharing, and advanced analysis.

Audience:

PhD students, Postdocs, (PIs). Expertise in programming not necessary. Prior experience with rodent MRI data acquisition and processing is a plus.

Methods:

The workshop will include interactive seminars given by selected experts in the field covering all aspects of (FAIR) small animal MRI data acquisition, analysis, and sharing. The seminars will be followed by hands-on training where participants will perform use case scenarios using software established by the organizers. This will include an introduction to the basics of using command line interfaces, Python installation, working with Docker/Singularity containers, Datalad/Git, and BIDS.

Schedule:
  • Scott Makeig, Institute for Neural Computation, University of California San Diego
    “Documenting temporal structure in time series data (evolving concepts, history, needs and benefits)”
    (45 mins + 10 minutes discussion)
  • Dung Truong, University of California San Diego
    “Adding annotations to neuroimaging data: the path from experiment to analysis: tools and pipelines”
    (50 mins + 10 min discussion)
  • Dora Hermes, Tal Pal Attia], Mayo Clinic, Rochester MN
    “Using the SCORE HED library schema for clinical EEG data annotation.”
    (30 mins + 10 min discussion)
  • Monique Denissen, University of Salzburg, Austria
    “Applying HED annotation to fMRI datasets: strategies and pitfalls.”
    (30 mins + 10 min discussion)

Research workflows for collaborative neuroscience

  • workflows
  • worklfow management
  • online tools
  • online platform
icon keynoteCo-Chair: Dimitri Yatsenko

To tackle the challenging questions of the brain, scientists are turning to innovative strategies to automate and organize their research workflows. Automated research workflows integrate computing infrastructure, automation, instrument integration, advanced data operations, and machine learning to enable researchers to process vast amounts of data and analyze complex patterns. This enhances the speed, accuracy, and reliability of collaborative research activities and enables scientists to generate new insights and knowledge that were previously unattainable. The implementation of automated research workflows is not just about technical solutions but also about creating novel team structures that foster collaboration and innovation. With these tools, researchers can work more efficiently, share knowledge more easily, and focus on the intellectual challenges of their research. In this workshop, general approaches to workflow management, team organizations, software tools, and online platforms and resources will be reviewed. Across various projects, from neurophysiology to genomics, these workflows are transforming the way that scientists conduct research, opening up new avenues for discovery and innovation.