Publications

Good scientific practice in EEG and MEG research: Progress and perspectives
Guiomar Niso
Laurens R. Krol
Etienne Combrisson
A. Sophie Dubarry
Madison A. Elliott
Clément François
Yseult Héjja-Brichard
Sophie K. Herbst
Karim Jerbi
Vanja Kovic
Katia Lehongre
Steven J. Luck
Manuel Mercier
John C. Mosher
Yuri G. Pavlov
Aina Puce
Antonio Schettino
Daniele Schön
Walter Sinnott-Armstrong
Bertille Somon … (see 6 more)
Anđela Šoškić
Suzy J. Styles
Roni Tibon
Martina G. Vilas
Marijn van Vliet
Maximilien Chaumon
Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce w… (see more)ork that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons.
2022-07-31
NeuroImage (published)
doi.org
Induced pluripotent stem cells display a distinct set of MHC I-associated peptides shared by human cancers
Anca Apavaloaei
Leslie Hesnard
Marie-Pierre Hardy
Basma Benabdallah
Gregory Ehx
Catherine Thériault
Jean-Philippe Laverdure
Chantal Durette
Joël Lanoix
Mathieu Courcelles
Nandita Noronha
Kapil Dev Chauhan
Sébastien Lemieux
Christian Beauséjour
Mick Bhatia
Pierre Thibault
Claude Perreault
2022-07-31
Cell Reports (published)
doi.org
Information Gain Sampling for Active Learning in Medical Image Classification
Raghav Mehta
Changjian Shui
Brennan Nichyporuk
Tal Arbel
2022-07-31
ArXiv (preprint)
doi.org
arxiv.org
Mortality trends and lengths of stay among hospitalized COVID-19 patients in Ontario and Québec (Canada): a population-based cohort study of the first three epidemic waves
Yiqing Xia
Huiting Ma
David L Buckeridge
Marc Brisson
Beate Sander
Adrienne Chan
Aman Verma
Iris Ganser
Nadine Kronfli
Sharmistha Mishra
Mathieu Maheu-Giroux
Epidemic waves of COVID-19 strained hospital resources. We describe temporal trends in mortality risk and length of stay in intensive cares … (see more)units (ICUs) among COVID-19 patients hospitalized through the first three epidemic waves in Canada. We used population-based provincial hospitalization data from Ontario and Québec to examine mortality risk and lengths of ICU stay. For each province, adjusted estimates were obtained using marginal standardization of logistic regression models, adjusting for patient-level characteristics and hospital-level determinants. Using all hospitalizations from Ontario (N=26,541) and Québec (N=23,857), we found that unadjusted in-hospital mortality risks peaked at 31% in the first wave and was lowest at the end of the third wave at 6-7%. This general trend remained after controlling for confounders. The odds of in-hospital mortality in the highest hospital occupancy quintile was 1.2 (95%CI: 1.0-1.4; Ontario) and 1.6 (95%CI: 1.3-1.9; Québec) times that of the lowest quintile. Variants of concerns were associated with an increased in-hospital mortality. Length of ICU stay decreased over time from a mean of 16 days (SD=18) to 15 days (SD=15) in the third wave but were consistently higher in Ontario than Québec by 3-6 days. In-hospital mortality risks and lengths of ICU stay declined over time in both provinces, despite changing patient demographics, suggesting that new therapeutics and treatment, as well as improved clinical protocols, could have contributed to this reduction. Continuous population-based monitoring of patient outcomes in an evolving epidemic is necessary for health system preparedness and response.
2022-07-31
International Journal of Infectious Diseases (published)
doi.org
A portrait of the different configurations between digitally-enabled innovations and climate governance
Pierre J. C. Chuard
Jennifer Garard
Karsten A. Schulz
Nilushi Kumarasinghe
David Rolnick
Damon Matthews
2022-07-31
Earth System Governance (published)
doi.org
Single‐pass stratified importance resampling
Ege Ciklabakkal
Adrien Gruson
Iliyan Georgiev
D. Nowrouzezahrai
Toshiya Hachisuka
Resampling is the process of selecting from a set of candidate samples to achieve a distribution (approximately) proportional to a desired t… (see more)arget. Recent work has revisited its application to Monte Carlo integration, yielding powerful and practical importance sampling methods. One drawback of existing resampling methods is that they cannot generate stratified samples. We propose two complementary techniques to achieve efficient stratified resampling. We first introduce bidirectional CDF sampling which yields the same result as conventional inverse CDF sampling but in a single pass over the candidates, without needing to store them, similarly to reservoir sampling. We then order the candidates along a space‐filling curve to ensure that stratified CDF sampling of candidate indices yields stratified samples in the integration domain. We showcase our method on various resampling‐based rendering problems.
2022-07-29
Computer Graphics Forum (published)
doi.org
Automated prediction of extubation success in extremely preterm infants: the APEX multicenter study
Lara J. Kanbar
Wissam Shalish
Charles C. Onu
Samantha Latremouille
Lajos Kovacs
Martin Keszler
Sanjay Chawla
Karen A. Brown
Doina Precup
Robert E. Kearney
Guilherme M. Sant’Anna
2022-07-28
Pediatric Research (published)
doi.org
BioCaster in 2021: automatic disease outbreaks detection from global news media
Zaiqiao Meng
Anya Okhmatovskaia
Maxime Polleri
Yannan Shen
Guido Powell
Zihao Fu
Iris Ganser
Meiru Zhang
Nicholas B. King
David Buckeridge
Nigel Collier
SUMMARY: BioCaster was launched in 2008 to provide an ontology-based text mining system for early disease detection from open news sources. … (see more)Following a 6-year break, we have re-launched the system in 2021. Our goal is to systematically upgrade the methodology using state-of-the-art neural network language models, whilst retaining the original benefits that the system provided in terms of logical reasoning and automated early detection of infectious disease outbreaks. Here, we present recent extensions such as neural machine translation in 10 languages, neural classification of disease outbreak reports and a new cloud-based visualization dashboard. Furthermore, we discuss our vision for further improvements, including combining risk assessment with event semantics and assessing the risk of outbreaks with multi-granularity. We hope that these efforts will benefit the global public health community. AVAILABILITY AND IMPLEMENTATION: BioCaster web-portal is freely accessible at http://biocaster.org.
2022-07-27
Bioinformatics (published)
doi.org
A Parsimonious Description of Global Functional Brain Organization in Three Spatiotemporal Patterns
Taylor Bolt
Jason S. Nomi
Danilo Bzdok
Jorge A. Salas
Catie Chang
B.T. Thomas Yeo
Lucina Q. Uddin
Shella D. Keilholz
Resting-state functional MRI has yielded seemingly disparate insights into large-scale organization of the human brain. The brain’s large-… (see more)scale organization can be divided into two broad categories - zero-lag representations of functional connectivity structure and time-lag representations of traveling wave or propagation structure. Here we sought to unify observed phenomena across these two categories in the form of three low-frequency spatiotemporal patterns composed of a mixture of standing and traveling wave dynamics. We showed that a range of empirical phenomena, including functional connectivity gradients, the task-positive/task-negative anti-correlation pattern, the global signal, time-lag propagation patterns, the quasiperiodic pattern, and the functional connectome network structure are manifestations of these three spatiotemporal patterns. These patterns account for much of the global spatial structure that underlies functional connectivity analyses, and unifies phenomena in resting-state functional MRI previously thought distinct.
2022-07-27
Nature Neuroscience (published)
doi.org
Explanatory latent representation of heterogeneous spatial maps of task-fMRI in large-scale datasets
Mariam Zabihi
Seyed Mostafa Kia
Thomas Wolfers
Stijn de Boer
Charlotte Fraza
Sourena Soheili-Nezhad
Richard Dinga
Alberto Llera Arenas
Danilo Bzdok
Christian F. Beckmann
Andre Marquand
Finding an interpretable and compact representation of complex neuroimage data can be extremely useful for understanding brain behavioral ma… (see more)pping and hence for explaining the biological underpinnings of mental disorders. Hand-crafted representations, as well as linear transformations, may not accurately reflect the significant variability across individuals. Here, we applied a data-driven approach to learn interpretable and generalizable latent representations that link cognition with underlying brain systems; we applied a three-dimensional autoencoder to two large-scale datasets to find an interpretable latent representation of high dimensional task fMRI image data. This representation also accounts for demographic characteristics, achieved by solving a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics (‘latent indices’) to find a multivariate mapping to non-imaging measures. We trained our model with multi-task fMRI data derived from the Human Connectome Project (HCP) that provides whole-brain coverage across a range of cognitive tasks. Next, in a transfer learning setting, we tested the generalization of our latent space on UK Biobank data as an independent dataset. Our model showed high performance in terms of age and predictions and was capable of capturing complex behavioral characteristics and preserving the individualized variabilities using a highly interpretable latent representation.
2022-07-26
bioRxiv (preprint)
doi.org
H4rm0ny: A Competitive Zero-Sum Two-Player Markov Game for Multi-Agent Learning on Evasive Malware Generation and Detection
Christopher Molloy
Steven H. H. Ding
Benjamin C. M. Fung
Philippe Charland
To combat the increasingly versatile and mutable modern malware, Machine Learning (ML) is now a popular and effective complement to the exis… (see more)ting signature-based techniques for malware triage and identification. However, ML is also a readily available tool for adversaries. Recent studies have shown that malware can be modified by deep Reinforcement Learning (RL) techniques to bypass AI-based and signature-based anti-virus systems without altering their original malicious functionalities. These studies only focus on generating evasive samples and assume a static detection system as the enemy.Malware detection and evasion essentially form a two-party cat-and-mouse game. Simulating the real-life scenarios, in this paper we present the first two-player competitive game for evasive malware detection and generation, following the zero-sum Multi-Agent Reinforcement Learning (MARL) paradigm. Our experiments on recent malware show that the produced malware detection agent is more robust against adversarial attacks. Also, the produced malware modification agent is able to generate more evasive samples fooling both AI-based and other anti-malware techniques.
2022-07-26
Computer Science Symposium in Russia (published)
doi.org
Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs
Stephen Bonner
Ufuk Kirik
Ola Engkvist
Jian Tang
Ian P Barrett
Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KG) that utilize… (see more) the interconnected nature of the domain. Graph-based modelling of the data, combined with KG embedding (KGE) methods, are promising as they provide a more intuitive representation and are suitable for inference tasks such as predicting missing links. One common application is to produce ranked lists of genes for a given disease, where the rank is based on the perceived likelihood of association between the gene and the disease. It is thus critical that these predictions are not only pertinent but also biologically meaningful. However, KGs can be biased either directly due to the underlying data sources that are integrated or due to modeling choices in the construction of the graph, one consequence of which is that certain entities can get topologically overrepresented. We demonstrate the effect of these inherent structural imbalances, resulting in densely-connected entities being highly ranked no matter the context. We provide support for this observation across different datasets, models as well as predictive tasks. Further, we present various graph perturbation experiments which yield more support to the observation that KGE models can be more influenced by the frequency of entities rather than any biological information encoded within the relations. Our results highlight the importance of data modeling choices, and emphasizes the need for practitioners to be mindful of these issues when interpreting model outputs and during KG composition.
2022-07-25
Briefings in Bioinformatics (unknown)
doi.org
arxiv.org