The FAIR principles, developed by the neuroscience community with the objective of promoting open science, are outlined by the International Neuroinformatics Coordinating Facility as ensuring data is findable, accessible, interoperable, and reusable.

These principles aim to enhance the ability of machines to find and use data, allowing individual researchers to cite data sets to inform more advanced studies — in short, how machines can solve the problem of having too much data to sort, in no single format.

Representing tool development initiatives, publishing, and data science, speakers from academia and industry across the globe come together in this workshop to present solutions for sharing, publishing, and collaborating in neuroscience.

They’ll cover the principles in detail, data standards and repositories, ethical and legal issues such as General Data Protection Regulation in the European Union, and Brain Imaging Data Structure, a way of organizing neuroimaging and behavioral data. From the publishing perspective, they’ll also propose questions to consider when publishing a paper, such as how to share your data and what information to make available.

To learn more about the best practices for data sharing, watch workshops on

FAIR Data Metadata and Data Sharing in Neurotrauma and improving reproducibility in neuroscience.

Speakers

Linda Lanyon, PhD, CEng, MBCS, CITP

Linda Lanyon is executive director of the International Neuroinformatics Coordinating Facility, an independent organization that promotes the field of neuroinformatics and aims to advance data reuse and reproducibility in global brain research. Prior to entering the field of neuroinformatics, Lanyon’s main research interests lay in computational modeling and neuroimaging investigation of visual attention. She received her undergraduate honors degree in computing and informatics and her PhD in computational neuroscience from Plymouth University, in the United Kingdom. She completed her postdoctoral fellowship in computational and empirical neuroscience at The University of British Columbia, in Canada.

Maryann Martone, PhD

Maryann Martone is a professor emerita at the University of California, San Diego, where she maintains an active laboratory. She started her career as a neuroanatomist, specializing in light and electron microscopy, but her research for the past 15 years has focused on neuroinformatics. She led the Neuroscience Information Framework (NIF), a national project to establish a uniform resource description framework for neuroscience, as well as the NIDDK Information Network (dknet). She is the current Chair of the Governing Board for the International Neuroinformatics Coordinating Facility and a founder of SciCrunch, a technology start creating tools to help publish reproducible science.

Gustav Nilsonne, MD, PhD

Gustav Nilsonne is an assistant professor at the Karolinska Institute and Stockholm University, in Sweden. Nilsonne’s main research interests lie in neuroscience (sleep and diurnal rhythms) as well as in meta-science (open data, reproducibility, and research synthesis). He received his MD and his PhD from the Karolinska Institute. He was a visiting scholar at Stanford University in 2017.

Kirstie Whitaker, PhD

Kirstie Whitaker is a research fellow at the Alan Turing Institute and senior research associate in the department of psychiatry at the University of Cambridge, in the United Kingdom. Whitaker's main research interests lie in understanding adolescent brain development using structural MRI, why the teenage years are critical to the emergence of mental health disorders, and how to better incentivize open science and reproducible research. She received her BSc in physics from the University of Bristol, her MSc in medical physics from the University of British Columbia, and her PhD in neuroscience from the University of California, Berkeley. She completed her postdoctoral training at the University of Cambridge.

Teon Brooks, PhD

Teon Brooks is a data scientist for Mozilla. He is a core contributor to the MNE Project, a collection of open-source data processing and analysis tools for MEG/EEG/iEEG. Brooks' main interests lie in neuroinformatics and software tool development. He received his bachelor of science in psychology and linguistics from the University of North Carolina at Chapel Hill and his master’s and PhD in cognition and perception from New York University. He completed a fellowship at Stanford University.

Michaela Torkar, PhD

Michaela Torkar is publishing director at F1000, where she is responsible for the set-up and development of new open science publishing platforms, which F1000 runs on behalf of funders such as the Wellcome Trust, the Bill & Melinda Gates Foundation, the Montreal Neurological Institut,e and other research organizations. She previously was editorial director at the open access publisher BioMed Central. She received her undergraduate degree in biology from the Ludwig Maximilians University of Munich, in Germany, and her PhD in immunology from the University of Cambridge, in the United Kingdom.

This lectures series provides an introduction to neuroinformatics.

Lessons in This Course

1. Welcome and Introduction to the INCF Short Course

This lecture covers an introduction to neuroinformatics and its subfields, the content of the short course, and future neuroinformatics applications.

2. Introduction to Microscopic Imaging

This lecture covers an introduction to connectomics and image processing tools for the study of connectomics.

3. Introduction to Neuromorphic Engineering

This lecture covers modeling the neuron in silicon, modeling vision and audition and sensory fusion using a deep network.

4. Introduction to Workflows for Model Simulation

This lecture gives an introduction to simulation, models, and the neural simulation tool NEST.

5. Introduction to Modeling the Brain

This lecture covers an introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

6. Introduction to Data Analysis and Neural Coding

This lecture covers describing and characterizing an input-output relationship.

7. Introduction to Neuroimaging

This lecture covers acquisition techniques, the physics of MRI, diffusion imaging, prediction using fMRI.

8. Introduction to Databases and Ontologies

This lecture covers structured data, databases, federating neuroscience-relevant databases, ontologies.

TrainingSpace (TS) is an online hub that aims to make neuroscience educational materials more accessible to the global neuroscience community. As a hub, TS provides users with access to:

• Multimedia educational content from courses, conference lectures, and laboratory exercises from some of the world’s leading neuroscience institutes and societies.
• Study tracks to facilitate self-guided study.
• Tutorials on tools and open science resources for neuroscience research.
• A Q&A forum.
• A neuroscience encyclopedia that provides users with access to over 1,000,000 publicly available datasets as well as links to literature references and scientific abstracts.

Topics currently included in TS include: general neuroscience, clinical neuroscience, computational neuroscience, neuroinformatics, computer science, data science, and open science.

All courses and conference lectures in TS include a general description, topics covered, links to prerequisite courses if applicable, and links to software described in or required for the course, as well as links to the next lecture in the course or more advanced related courses. To learn more about TrainingSpace, visit: https://training.incf.org/

Material below summarizes the article Functional Connectome Analyses Reveal the Human Olfactory Network Organization, published on May 29, 2020, in eNeuro and authored by T. Campbell Arnold, Yuqi You, Mingzhou Ding, Xi-Nian Zuo, Ivan de Araujo and Wen Li.

Highlights

• Smell is borne out of a network of interconnected regions that are distributed across the brain.

• The larger odor processing network consists of three smaller subnetworks, which serve different functional purposes.

• Segregation of subnetworks helps to insulate the different functions and facilitates olfactory perception.

Study Question

What is the brain architecture for human olfaction? How are widely distributed neural structures organized to support the uniquely multifaceted functions of olfaction?

How This Research Advances What We Know

Human olfaction is complex, varied, and extends beyond smell into homeostatic functions of emotion, feeding, and endocrine. However, among all five senses, the sense of smell is the least studied and most mysterious. While the brain networks underlying our senses of sight, hearing, and touch have all been unveiled, the network for smell remains elusive. Here, we addressed this critical gap by characterizing the brain network of smell ("the smell network"). We also examined whether the smell network is optimally organized to enhance our olfactory perception.

Experimental Design or Methodology

To understand how the brain regions are connected, neuroscientists record brain activity and isolate regions that are active at the same time. The different groups of coactive brain regions form networks, which have been associated with carrying out specialized tasks, such as directing attention, processing emotions, or interpreting sensory information.

We thus examined brain coactivation ("coupling") in two datasets. First, in a large sample of 812 individuals, we compared brain activity from different regions and compiled a list of areas associated with processing smells. To understand how these regions were organized into a "smell network," we then conducted graph-theoretical analyses. In the context of the brain, a graph is a set of brain regions (called nodes) and the connections between regions (called edges). Brain regions that carry out similar tasks are often densely interconnected by edges, forming what are called local communities within the larger network. Communication between these local communities tends to be facilitated by a select few nodes, which are referred to as critical nodes or network hubs. We analyzed the "smell network" to reveal the local communities and critical nodes that may influence odor processing.

In the second dataset of 32 individuals, we applied the graphic organization isolated above to their brain activity and correlated the organization efficiency indices with their performance on an odor discrimination task.

Results

We found that the smell network comprised brain regions from the frontal and temporal lobes. This network contained three communities (subnetworks), which have dense internal connections and are somewhat insulated from the other subnetworks. These subnetworks are composed in a manner closely aligned with the key facets of olfaction, including sensory processing (the yellow subnetwork), emotional processing (the red subnetwork), and decision making (the blue subnetwork). Nonetheless, these subnetworks maintain a certain degree of communication among each other via two "hubs," which were the amygdala (AMY) and anterior insula (INSa). This organization of tightly knit subnetworks that are externally linked by two hubs represents a high degree of network efficiency. Importantly, the degree that the subnetworks maintain internal connection and external insulation in an individual predicted their ability to discern distinct smells. Participants with more tightly clustered local communities were better able to discriminate different smells than individuals with more inter-community integration. The relationship between network organization and odor discrimination indicates the importance of distinct network communities, and the unique functional role of each community, in odor processing.

Interpretation

We characterized a large-scale network of brain regions, revealing the brain's architecture of smell. The composition and organization of this smell network supports the incredibly rich and complex sense of smell. Defined through a remarkably large and heterogeneous sample, this network can serve as a representative, reliable anatomical template to guide future research into the sense of smell. 

Visit eNeuro to read the original article and explore other content. Read other summaries of eNeuro and JNeurosci papers in the Neuronline collection SfN Journals: Research Article Summaries.

Functional Connectome Analyses Reveal the Human Olfactory Network Organization. T. Campbell Arnold, Yuqi You, Mingzhou Ding, Xi-Nian Zuo, Ivan de Araujo and Wen Li eNeuro 29 May 2020, 7 (4) ENEURO.0551-19.2020; DOI: 10.1523/ENEURO.0551-19.2020 

About the Contributors

Thomas Campbell Arnold

Thomas Campbell Arnold is a bioengineering PhD student at the University of Pennsylvania. He obtained BS in mathematics and biological sciences at Florida State University.

Wen Li, PhD

Wen Li is currently an associate professor of psychology and neuroscience at Florida State University and completed her PhD at Northwestern University.

Imagine you are the parent of two children. Your oldest, a boy, was diagnosed with autism last year and just celebrated his fourth birthday. Your youngest, a girl, is six months old. You’ve heard that autism runs in families; you know this means that your daughter is at higher risk than most children. But you’ve also heard that boys tend to get autism at a higher rate than girls. Your daughter, like her brother, is a poor sleeper, and sometimes you wonder whether she is more interested in looking at the ceiling fan than at you…but other times she smiles at you or her brother and seems very engaged. You find yourself making comparisons between your two children frequently, and wondering…will she have autism too?

A friend tells you that just last year, researchers were able to scan the brains of babies when they were six to twelve months old and predict, for the first time, who would develop autism by age two. Your friend then poses the inevitable question: would you want this test for your daughter?

Predicting disease before the onset of symptoms has long been a goal of medical research. The idea is simple: identify those at highest risk, intervene earlier, and thereby (hopefully) improve outcomes. Historically, MRI and other neuroimaging modalities have been used to detect current pathology (i.e., tumors, seizure activity), but have not proven very effective for clinical prediction. More recently, the move towards larger sample sizes and more sophisticated statistical approaches—like machine-learning based classification—have enabled researchers to predict, with high accuracy, who will develop a disorder based on brain data alone.

The move towards more sophisticated statistical approaches have enabled researchers to predict, with high accuracy, who will develop a disorder based on brain data alone. MRI for clinical prediction holds great promise. MRI remains expensive compared to other neuroimaging modalities like EEG, but MRI scanners are present in most hospitals, and innovations in scanner technology mean smaller, more portable scanners may soon be available. It is easy to see a future in which affordable, widely-available MRI technology plays an integral role in predicting diseases that affect brain structure and function. However, the power to detect disease before the onset of symptoms brings with it the responsibility to carefully consider the implications of that prediction. This responsibility is even greater when a predictive test is conducted with a child who is incapable of making decisions about their own future and must instead rely on parents to make decisions on their behalf.

Thankfully, neuroscientists are not the first to encounter difficult questions about disease prediction. Presymptomatic prediction has been a topic in genome sciences for decades. Clinical medicine has been revolutionized by our ability to detect from birth a number of rare genetic diseases that, if treated immediately, can save lives and improve outcomes. Genetic testing has also been used to predict brain disorders like Huntington’s disease—a neurological disorder with onset in midlife that, unfortunately, cannot be prevented or treated. Perhaps because of the lack of treatment, uptake of testing for Huntington’s is very low: one UK study estimated that only 17% of those at risk for Huntington’s disease choose to get tested.

So to return to the thought exercise we began with: imagine you are this parent, with an older child with autism and a new baby who is at higher risk. Would you be interested in obtaining a predictive test to tell you if your baby will develop autism? Autism is a spectrum disorder, and individuals with autism can have a broad range of strengths and weaknesses. Early behavioral interventions for children with autism have been shown to be effective for improving social and cognitive skills. However, there are not yet any interventions for babies as young as six months old. So, as a parent, what might it mean to learn this predictive information and not immediately know what to do with it?

Historically, the genetics literature has recommended that predictive testing in childhood should only occur for disorders that have effective treatments. More recently, there is growing advocacy for a different approach—one which prioritizes personal utility of predictive results over strictly medical utility. Parents of children with Fragile X (a neurodevelopmental disorder that can be detected based on genetic test at birth, but which does not emerge with full symptoms until close to age two) advocate for including Fragile X along with other disorders that are tested for at birth. Even though there is no proven treatment for Fragile X, parents say that the information can still be useful for avoiding a prolonged “diagnostic odyssey” once symptoms emerge; it can also give families more time to adjust expectations and prepare to support their child with special needs, including enrolling in supportive services like speech or physical therapy.

The power to detect disease before the onset of symptoms brings with it the responsibility to carefully consider the implications of that prediction.

For autism, the days of predictive testing are still in the future. The predictive MRI approach first pioneered by the Infant Brain Imaging Study (IBIS) is currently being validated, and until it is proven that the predictive algorithm generalizes to a new group of infants, there are no plans to disclose results to families. In the meantime, I and others are working to fully understand the ethical implications of MRI-based predictive testing for disorders in childhood. While similar in some ways to the issues raised by predictive genetic testing, there are unique aspects of predictive MRI that deserve consideration—for example, conceptualizing a child’s disorder as a “brain disorder” has been associated with more pessimism about their future functioning.

As with many challenges in the ever-evolving world of neuroethics, this one combines a rapidly advancing technology with some age-old human questions about autonomy, health and identity. Ultimately, my hope is that this work will help to ensure that the technical innovations of clinical neuroscience are able to maximally benefit the lives of children and their families.

About the Contributor

Kate MacDuffie, PhD

Kate MacDuffie is a Licensed Clinical Psychologist, a Postdoctoral Fellow at the University of Washington, and a Bioethics Fellow at the Treuman Katz Center for Pediatric Bioethics at Seattle Children’s.

Read the latest research on the use of neuroimaging to interrogate neural circuits:

• Pushing the Boundaries of Psychiatric Neuroimaging to Ground Diagnosis in Biology
• Toward Robust Functional Neuroimaging Genetics of Cognition
• Functional Connectome Analyses Reveal the Human Olfactory Network Organization
• Functional Connectome of the Fetal Brain
• Resolving the Connectome, Spectrally-Specific Functional Connectivity Networks and Their Distinct Contributions to Behavior
• Decoding Task-Specific Cognitive States with Slow, Directed Functional Networks in the Human Brain