By Bin Lu and Niall Duncan
Recent years have seen a number of important themes come to the attention of the global neuroimaging community. The robustness of findings reported in the literature have been questioned as people begin to focus more on reproducibility and other statistical issues. At the same time, more attention is being paid to the variability between individuals, not least as efforts to develop diagnostic tools for different brain diseases advance. Databases of imaging data from very large samples have come to the fore as one way of tackling these issues and have already led to some striking results.
Researchers working in China are leading a number of these large-scale initiatives. In all, several thousands of participants have been scanned to acquire various MRI image types. These have been used to produce resources that are openly available to all. Here, we provide a brief overview of some of these resources to bring them to the attention of the community and let people know what is available to work with now, and what will be coming out in the near future.
Investigating the changes in the brain across the lifespan is a difficult endeavour but will help us understand how these changes affect us in health and disease. Large datasets are particularly useful in this context as they can capture the variability in developmental trajectory seen across the population. Understanding the brain in later life is a particularly prominent question within countries, such as China, that have rapidly aging populations.
The Southwest University Adult Lifespan Dataset (SALD) includes data from 494 individuals spanning an age range of 19 to 80 years. Each person has a T1-weighted anatomical image and a resting-state functional scan, along with rich phenotypic information available for download. This represents the largest raw data resource currently available involving participants living in China.
Two other large aging and development related initiatives are currently ongoing. The Beijing Aging Brain Rejuvenation Initiative (BABRI) project has been running for over a decade and has so far obtained multimodal imaging data from several thousand people over 50 years of age in the Beijing area. Each person also completes a battery of neuropsychological tests and various psychological questionnaires. The project, run by Beijing Normal University, aims to scan a total of 5000+ people. The Colour Nest Project, run by the Chinese Academy of Sciences Institute of Psychology is a longitudinal MRI project of participants aged between 6 to 84 years, and aims to scan up to 1200 people three times between 2016 and 2022.
Testing this sort of measurement reliability is also the aim of the Southwest University Longitudinal Imaging Multimodal (SLIM) dataset. This is a test-retest resource obtained from 241 young participants. Each person was scanned three times over a three and a half year period, with each session including anatomical, diffusion-weighted, and resting-state fMRI scans. It is also the aim of the global Consortium for Reliability and Reproducibility (CoRR) to which researchers based in China have been contributing and which has been partly led out of the Chinese Academy of Sciences. This dataset includes a large number of anatomical, diffusion weighted, rs-fMRI, and cerebral blood flow images from centres in China and around the world.
Hosting MRI data can be expensive and complicated due to the large amount of storage space required, especially as one gets to subject counts in the thousands. The R-fMRI Maps Project, run out of the Institute of Psychology at the Chinese Academy of Sciences, seeks to reduce this problem by hosting the final indices calculated on resting-state data, rather than the data itself. Standardised pipelines are applied to the data by researchers to produce these indices and then the relatively small resulting files can be easily uploaded, along with other data such as demographics or cognitive test scores. This approach also has the advantage of reducing some of the privacy concerns associated with publicly sharing raw data.
One of the sets of indices hosted at the R-fMRI Maps Project is the REST-meta-MDD dataset. This represents one of the largest major depressive disorder (MDD) patient and control resources in the world with 2428 participants included (1300 patients) from sites all over China. The same processing pipeline was applied to all the participants and the resulting indices then uploaded to the central server. This resource is likely to be of great use in efforts to understand the variability contained within the MDD diagnosis.
Finally, the standard brain templates used in most neuroimaging analyses are made from one person or from small samples of people of European descent. There may be morphological differences between these templates and many of the people living in China that could affect the results of analyses. To address this problem the Chinese2020 project obtained anatomical images from 1000 people in China and Hong Kong to create a brain template for the majority population in that region. The template is freely available for use, as is a conversion between it and MNI space.
As can be seen, there are many exciting projects going on in China, generating large amounts of data that is (or will be) available to researchers to investigate. These datasets are targeted at some of the main questions neuroimagers are currently focused on and have the potential to greatly advance our understanding of, amongst other things, brain development, aging, and psychiatric disorders.
“Bringing Great Minds Together and Signaling to OHBM in Rome”, Human Brain Mapping Israel 1st Conference
Israel is a small country, approximately 400 km long north to south and 25 km width at its narrowest point. Despite its small size, Israel is home to six large universities and this year hosted the 1st Human Brain Mapping conference. This inaugural conference aimed to bring together neuroimaging researchers from each of these universities, to share ideas and methods. The conference unites those working on a number of different modalities - as was shown by the diversity in over 70 talks and posters, with research using MRI, fNIRS, MEG, EEG and brain stimulation, studying populations across the lifespan.
The conference covered a wide array of computational tools to analyze neuroimaging data (deep learning algorithms, multi-variate pattern analysis, variability quenching etc), unique sequences for structural mapping, and applications of the above methods to clinical and healthy populations. Researchers presented studies on the therapeutic effect of TMS, for example, to reduce alcoholism symptoms, as well as other brain stimulation techniques such as tDCS, multi-unit electrodes, and deep TMS.
As a preview for the OHBM conference in Rome, a special session was dedicated to the developing brain. This session focused on functional MRI studies during reading and screen exposure in children. The researchers discussed the neural networks related to changes in the use of visual and language-related regions during development with the exposure to reading (Dr Bitan), the critical changes in neural circuits supporting memory along development (Dr Ofen) and the “competition” on these neural networks while exposed to screens in childhood (Dr Horowitz-Kraus). The session also highlighted the importance of mother-child joint attention for social and emotional development and the effect this interaction has on babies’ neural activity coherence patterns during rest (Dr Frenkel). These topics were expanded in the Neurobehavioral basis of Development session, chaired by Luna Beatriz in OHBM in Rome. In this session Niko Dosenbach demonstrated exciting new fMRI analysis techniques that could estimate functional connections within and between neural networks at the single subject level in children. Using this technique, he was able to reveal several networks, previously seen at group-level, including cingulo-opercular and fronto-parietal networks. His talk was followed by fascinating presentations by Drs Satterthwaite and Beatriz on the conjunction of behavior with structural (diffusion) and neurochemical (spectroscopy) neuroimaging data in relation to mental health and development. This, combined with a large sample of data (ABCD database, Damien Fair), left the audience with the feeling that this is just the tip of the iceberg.
Other intriguing topics presented in the Israeli conference included several unique methods applied to structural neuroimaging data: from differentiating the six layers of the cortex (aka cortical layering), and using an MR sequence that provides the caliber of the axons in humans, presented by Dr Yaniv Assaf and his students, to quantitative T1 mapping presented by Dr Mezer. Some of these methods were extended to a discussion about structural plasticity in the session in Rome, chaired by Dr Monika Schonauer, which focused on changes in diffusion weighted measures (Dr Brodt), plasticity of diffusion weighted measures in relation to motor learning (Drs Maggiore and Johansen-Berg) and to the dynamic of the connectome (Dr Assaf). Both topics of developmental neuroimaging and innovative structural neuroimaging methods were merged in a fascinating keynote given in Rome by Dr Armin Raznahan, discussing sex-related differences in structural neuroimaging data (anatomical T1 data) in children.
Israel, one of the leading countries in applications and industry development, is also known as the “start-up nation”. With several developments related to brain stimulation, machine learning algorithm applications to human brain mapping, a strong hub of human brain mappers across populations, ages, and techniques may mutually fertilize both researchers in academia and industry. “These annual meetings, which will continue occurring before the official OHBM conference, allow a unique opportunity to students and researchers with a variety of specialties focusing on the human brain, to interact, collaborate and comment on each other’s work” says Dr Porat. As a small geographical area with many stimulating brains, the ability to bring these brains together to make more than the sum of their parts during this conference was welcome, and we look forward to more exciting developments in human brain mapping in Israel. For more information see https://elsc.huji.ac.il/events/718
By Johannes Algermissen, James Bartlett, Remi Gau, Stephan Heunis, Eduard Klapwijk, Matan Mazor, Mariella Paul, Antonio Schettino, David Mehler
The neuroimaging field has recently seen a substantial surge in new initiatives that aim to make research practices more robust and transparent. At our annual OHBM meetings you will have likely come across the Open Science room. While many aspects fall under the umbrella term Open Science, for this post we focus on research practices that aim to make science more replicable and reproducible. These include non peer-reviewed study preregistration, peer-reviewed registered reports that reward researchers’ study plan with in-principle acceptance before data collection, but also code and data sharing tools such as NeuroVault and OpenNeuro.
As neuroimagers, we work closely with and learn from other disciplines, including Psychology. One place where a lot of grassroot development has come to fruition in recent years is the annual meeting of the Society for the Improvement of Psychological Science (SIPS). SIPS breaks with the traditional conference format and focuses on practical work, peer projects and solving concrete problems in groups. The SIPS experience can feel a bit like a playground for research practice geeks: participants sit in the driver's seat and can pick from a variety of so-called unconferences where they pitch and debate ideas to reform research practices, hackathons where everyone can contribute their “bits” and thoughts, and workshops where you can catch up on learning to use the latest R packages or Bayesian analysis. In this vibrant setting we embarked as a group of enthusiastic neuroimagers on an expedition to intermingle with other open science crowds. We wanted to find out how study preregistration and registered reports could be tailored more towards neuroimaging studies. Prepared with a list of challenges that we learned about through our informal survey, we felt determined to provide more clarity around adequate statistical power in our field, and strived to ultimately come up with a potential user-friendly template for preregistration of neuroimaging studies. We completed some initial steps at the hackathon and the immediate aftermath with a focus on tools that help researchers preregister their studies. Here, we summarize our group projects and provide you with some (interim) outcomes.
Collection of preregistrations and registered reports in neuroimaging
Preregistration and registered reports are ways to state in advance what your hypothesis is and how you are planning to run and analyze the study. They are meant as tools to prevent researchers’ own cognitive bias (e.g., hindsight bias or confirmation bias) hijacking their investigation. They are not meant to stifle exploration but to make very explicit what part of a study was confirmatory and what part was exploratory (see http://cos.io/prereg/ and http://cos.io/rr/ for more details). Preregistration protocols have been around for a while for clinical trials but they have only started in the past few years to be on the radar of psychology researchers. The uptake seems to have been much slower in research involving (f)MRI, EEG, or MEG. Apart from the large amount of methodological and analytical detail needed to preregister neuroimaging studies, one reason may be the lack of examples of what a preregistration in those fields could look like. Those M/EEG and fMRI preregistrations and registered reports scattered on the internet are also hard to find. Therefore, during the hackathon, we started a list of all the openly available neuroimaging preregistrations and registered reports. This resulted in a spreadsheet, accompanied by keywords to make it easier to select relevant ones you are interested in. This document is still a work in progress and we welcome contributions to this potentially ever-growing list, especially if we missed one of your own preregistrations! Simply use this form to add an entry. We hope that such an easily accessible list of preregistrations will inspire many more neuroscientists to preregister their studies and will help to establish best practices.
BrainPower: resources for power analysis in neuroimaging
Every planning phase of an empirical neuroimaging research project should consider sample size and statistical power: How big is the effect that I am interested in? How likely am I to observe it given the resources (number of participants, number of trials) at my disposal? Power analysis should provide clarity on these questions. It might appear relatively easy for simple designs with one-dimensional behavioural variables, especially with the help of programs such as G*Power and standard effect size measures such as Cohen's d. However, the high-dimensional nature of neuroimaging data and designs (processing three-dimensional data over time with mass univariate and multivariate approaches) requires additional steps, e.g., cluster correction, to prevent false-positive inference. And our understanding of "effects" based on these data and methods is not necessarily as intuitive: how strong should the level of activation be, or how large should the cluster be?
One important approach to power analysis is simulations: When taking resting-state data and adding an activation of a certain size and extent, can I reliably find the effect? This approach has been facilitated by advances in computational power and new software in recent years, allowing researchers to have full control of the ground-truth. Alternative approaches to estimate effect sizes is relying on past literature (which may provide biased estimates) and re-using existing, or even open data sets.
For both approaches, experts have created primers, tools, and software. Unfortunately, their use may not always seem intuitive. Further, researchers might have a hard time recognizing which tools best suits their specific needs. We thus collated a variety of such tools, compared these different approaches, and described their use (“how to”) to empirical researchers. Overall we gather collection that provides:
This list of resources is openly available and still growing in content. The immediate future goal is to expand the resources with tutorials and work examples of conducting power analyses on real and simulated fMRI data. We then plan to formalise these resources into a website. We invite and welcome any and all contributions from the community!
A new way to calibrate the smallest effect size of interest (SESOI) for neuroimaging, using an fMRI example
Adequate sample size planning is crucial to make good use of resources and draw valid inferences from imaging data. One-size-for-all recommended sample sizes are slowly being replaced by power analysis procedures that are based on effect sizes that seem reasonable. In the more common approach, effect sizes are estimated based on available data or previous studies. However, this approach does not account for the ability to necessarily detect a meaningful effect size. An alternative approach is to power studies sufficiently to detect the smallest effect size of interest (SESOI), thereby increasing the chance to find an effect that is meaningful for the research question (e.g., for practical, or theoretical reasons). Also, in the event of a non-significant (i.e., “null”) finding, this approach increases the chances to reject negligible effect sizes, rendering “null findings” more informative. Hence, while this approach is more rigorous, it often requires larger samples, especially when studying higher order cognitive functions where group effect sizes are known to be small. On the other hand, running too many participants also comes with a cost: scanner time is an expensive resource of limited availability. Identifying a procedure that can balance this trade-off would thus be desirable and potentially help researchers to implement a sampling plan that is based on a SESOI.
We thus started with the following thought experiment: in an attempt to optimize sample sizes for specific experiments and statistical tests, one can capitalize on the fact that neuroimaging data is rich and affords numerous statistical tests that are statistically orthogonal. It is safe to assume that some sources of noise are shared between contrasts, within a participant (for example, a participant that moves a lot in the scanner will have more noisy parameter estimates), and that other sources are shared between participants within the same lab (for example, the quality of the scanner). Based on these two points, we envision a dynamic procedure for sample size specification that is sensitive to the noise in the specific sample of participants. Implementing such a procedure seems fairly simple: data acquisition stops exactly when a group-level contrast that is orthogonal to the ones of interest reaches a pre-specified significance level in a pre-specified region of interest.
A preregistration template for EEG
Analyzing neuroimaging data involves a myriad of decisions that researchers often consider only after data collection. When preregistering a neuroimaging study, thinking of each detail of the analysis can be challenging, especially because current available preregistration templates are generic and do not ask for the relevant technical details and specifications that are relevant for EEG experiments. For example, preprocessing EEG data involves many decisions - including resampling, filtering, and artefact rejection - that can have a profound impact on the results.
As part of the hackathon, we started to create a preregistration template for EEG studies that highlights such decisions during preprocessing and statistical analysis. For instance, the user is reminded to describe the electrode type and brand, data import, resampling, filtering, epoching, artefact detection/rejection/correction procedures, baseline correction, and averaging. The current version of the template is a text document based on the standard OSF preregistration form where we added specific questions about preprocessing and analysis steps for event-related potentials (ERPs). This EEG preregistration template is an ongoing project. If you have worked with EEG data or preregistrations before, your input would be highly appreciated! Ultimately, we aim to include the finished template on the OSF list of preregistration forms and extend the preregistration template to other analyses of EEG data (e.g., time-frequency analyses).
To wrap up, SIPS certainly provides a great opportunity for neuroimagers to intermingle with others and contribute to projects related to scientific practices in an open, inclusive, and dynamic environment. Anyone can pitch a session ad-hoc for the next day and the outcome of each project is openly documented on the OSF. This ensures that projects like ours on preregistration and neuroimaging can develop and live a happy after-conference life.