Publications

Functional connectivity subtypes associate robustly with ASD diagnosis
Sebastian G. W. Urchs
Angela Tam
Pierre Orban
Clara A. Moreau
Yassine Benhajali
Hien Duy Nguyen
Alan C. Evans
Pierre (Louis) Bellec
Our understanding of the changes in functional brain organization in autism is hampered by the extensive heterogeneity that characterizes th… (see more)is neurodevelopmental disorder. Data driven clustering offers a straightforward way to decompose autism heterogeneity into subtypes of connectivity and promises an unbiased framework to investigate behavioral symptoms and causative genetic factors. Yet, the robustness and generalizability of functional connectivity subtypes is unknown. Here, we show that a simple hierarchical cluster analysis can robustly relate a given individual and brain network to a connectivity subtype, but that continuous assignments are more robust than discrete ones. We also found that functional connectivity subtypes are moderately associated with the clinical diagnosis of autism, and these associations generalize to independent replication data. We explored systematically 18 different brain networks as we expected them to associate with different behavioral profiles as well as different key regions. Contrary to this prediction, autism functional connectivity subtypes converged on a common topography across different networks, consistent with a compression of the primary gradient of functional brain organization, as previously reported in the literature. Our results support the use of data driven clustering as a reliable data dimensionality reduction technique, where any given dimension only associates moderately with clinical manifestations.
2022-10-04
eLife (published)
doi.org
Protective effectiveness of prior SARS-CoV-2 infection and hybrid immunity against Omicron infection and severe disease: a systematic review and meta-regression
Niklas Bobrovitz
Harriet Ware
Xiaomeng Ma
Zihan Li
Reza Hosseini
Christian Cao
Anabel Selemon
Mairead Whelan
Zahra Premji
Hanane Issa
Brianna Cheng
L. Abu-Raddad
David Buckeridge
M. D. Kerkhove
Vanessa Piechotta
Melissa M Higdon
Annelies Wilder-Smith
Isabel Bergeri
Daniel R Feikin
Rahul K. Arora … (see 2 more)
Minal K Patel
Lorenzo Subissi
Background We aimed to systematically review the magnitude and duration of the protective effectiveness of prior infection (PE) and hybrid i… (see more)mmunity (HE) against Omicron infection and severe disease. Methods We searched pre-print and peer-reviewed electronic databases for controlled studies from January 1, 2020, to June 1, 2022. Risk of bias (RoB) was assessed using the Risk of Bias In Non-Randomized Studies of Interventions (ROBINS-I)-Tool. We used random-effects meta-regression to estimate the magnitude of protection at 1-month intervals and the average change in protection since the last vaccine dose or infection from 3 months to 6 or 12 months. We compared our estimates of PE and HE to previously published estimates of the magnitude and durability of vaccine effectiveness (VE) against Omicron. Findings Eleven studies of prior infection and 15 studies of hybrid immunity were included. For prior infection, there were 97 estimates (27 at moderate RoB and 70 at serious RoB), with the longest follow up at 15 months. PE against hospitalization or severe disease was 82.5% [71.8-89.7%] at 3 months, and 74.6% [63.1-83.5%] at 12 months. PE against reinfection was 65.2% [52.9-75.9%] at 3 months, and 24.7% [16.4-35.5%] at 12 months. For HE, there were 153 estimates (78 at moderate RoB and 75 at serious RoB), with the longest follow up at 11 months for primary series vaccination and 4 months for first booster vaccination. Against hospitalization or severe disease, HE involving either primary series vaccination or first booster vaccination was consistently >95% for the available follow up. Against reinfection, HE involving primary series vaccination was 69.0% [58.9-77.5%] at 3 months after the most recent infection or vaccination, and 41.8% [31.5-52.8%] at 12 months, while HE involving first booster vaccination was 68.6% [58.8-76.9%] at 3 months, and 46.5% [36.0-57.3%] at 6 months. Against hospitalization or severe disease at 6 months, hybrid immunity with first booster vaccination (effectiveness 95.3% [81.9-98.9%]) or with primary series alone (96.5% [90.2-98.8%]) provided significantly greater protection than prior infection alone (80.1% [70.3-87.2%]), first booster vaccination alone (76.7% [72.5-80.4%]), or primary series alone (64.6% [54.5-73.6%]). Results for protection against reinfection were similar. Interpretation Prior infection and hybrid immunity both provided greater and more sustained protection against Omicron than vaccination alone. All protection estimates waned quickly against infection but remained high for hospitalisation or severe disease. Individuals with hybrid immunity had the highest magnitude and durability of protection against all outcomes, reinforcing the global imperative for vaccination.
2022-10-04
medRxiv (preprint)
doi.org
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods
Unsupervised Domain Adaptation (UDA) aims at classifying unlabeled target images leveraging source labeled ones. In this work, we consider t… (see more)he Partial Domain Adaptation (PDA) variant, where we have extra source classes not present in the target domain. Most successful algorithms use model selection strategies that rely on target labels to find the best hyper-parameters and/or models along training. However, these strategies violate the main assumption in PDA: only unlabeled target domain samples are available. Moreover, there are also inconsistencies in the experimental settings - architecture, hyper-parameter tuning, number of runs - yielding unfair comparisons. The main goal of this work is to provide a realistic evaluation of PDA methods with the different model selection strategies under a consistent evaluation protocol. We evaluate 7 representative PDA algorithms on 2 different real-world datasets using 7 different model selection strategies. Our two main findings are: (i) without target labels for model selection, the accuracy of the methods decreases up to 30 percentage points; (ii) only one method and model selection pair performs well on both datasets. Experiments were performed with our PyTorch framework, BenchmarkPDA, which we open source.
2022-10-03
ArXiv (preprint)
doi.org
arxiv.org
Revisiting the Impact of Anti-patterns on Fault-Proneness: A Differentiated Replication
Aurel Ikama
Vincent Du
Philippe Belias
Biruk Asmare Muse
Foutse Khomh
Mohammad Hamdaqa
Anti-patterns manifesting on software code through code smells have been investigated in terms of their prevalence, detection, refactoring, … (see more)and impact on software quality attributes. In particular, leveraging heuristics to identify fault-fixing commits, Khomh et al. have found that anti-patterns and code smells have an impact on the fault-proneness of a software system. Similarly, Saboury et al. found a relationship between anti-pattern occurrences and fault-proneness, using heuristic to identify fault-fixing commits and fault-inducing changes. However, recent studies question the accuracy of heuristics, and thus the validity of empirical studies that leverage it. Hence, in this work, we would like to investigate to what extent the results of empirical studies using heuristics to identify bug fix commits are affected by the limitations of the heuristics based approach using manually validated bug fix commits as a ground truth. In particular, we conduct a differentiated replication of the work by Khomh et al. We particularly focused on the impact of anti-patterns on fault-proneness as it is the only dependent variable that may be affected by noise in the collected faults data. In our differentiated replication study, (1) we expanded the number of subject systems from 5 to 38, (2) utilized a manually validated dataset of bug-fixing commits from the work of Herbold et al., and (3) answered research questions from Khomh et al., that are related to the relationship between anti-pattern occurrences and fault-proneness. (4) We added an additional research question to investigate if combining results from several heuristic-based approaches could help reduce the impact of noise. Our findings show that the impact of the noise generated by the automatic algorithm heuristic based is negligible for the studied subject systems; meaning that the reported relation observed on noisy data still holds on the clean data. However, we also observed that combining results from several heuristic based approaches do not reduce this noise, quite the contrary.
2022-10-03
2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM) (published)
doi.org
Image-Guided Brachytherapy for Rectal Cancer: Reviewing the Past Two Decades of Clinical Investigation
T. Vuong
Aurelie Garant
Veronique Vendrely
Remi Nout
André-Guy Martin
Shirin A. Enger
Ervin Podgorsak
Belal Moftah
S. Devic
2022-10-01
Cancers (published)
doi.org
Lifelong Topological Visual Navigation
Rey Reza Wiyatno
Anqi Xu
Liam Paull
Commonly, learning-based topological navigation approaches produce a local policy while preserving some loose connectivity of the space thro… (see more)ugh a topological map. Nevertheless, spurious or missing edges in the topological graph often lead to navigation failure. In this work, we propose a sampling-based graph building method, which results in sparser graphs yet with higher navigation performance compared to baseline methods. We also propose graph maintenance strategies that eliminate spurious edges and expand the graph as needed, which improves lifelong navigation performance. Unlike controllers that learn from fixed training environments, we show that our model can be fine-tuned using only a small number of collected trajectory images from a real-world environment where the agent is deployed. We demonstrate successful navigation after fine-tuning on real-world environments, and notably show significant navigation improvements over time by applying our lifelong graph maintenance strategies.
2022-10-01
IEEE Robotics and Automation Letters (published)
doi.org
arxiv.org
MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record
Yuri Ahuja
Yuesong Zou
Aman Verma
David Buckeridge
Yue Li
2022-10-01
Journal of Biomedical Informatics (published)
doi.org
Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI.
Thierry L. Lefebvre
Ozan Ciga
Sahir Rai Bhatnagar
Yoshiko Ueno
S. Saif
Eric Winter-Reinhold
Anthony Dohan
P. Soyer
Reza Forghani
Kaleem Siddiqi
Jan Seuntjens
Caroline Reinhold
Peter Savadjiev
2022-10-01
Diagnostic and Interventional Imaging (published)
doi.org
Synthetic data as an enabler for machine learning applications in medicine
Jean-Francois Rajotte
Robert Bergen
David Buckeridge
Khaled El Emam
Raymond Ng
Elissa Strome
2022-10-01
iScience (published)
doi.org
The use of artificial intelligence and virtual reality in doctor-patient risk communication: A scoping review.
Ryan Antel
Samira Abbasgholizadeh-Rahimi
Elena Guadagno
Jason M. Harley
Dan Poenaru
2022-10-01
Patient Education and Counseling (published)
doi.org
Evolution of cell size control is canalized towards adders or sizers by cell cycle structure and selective pressures
Felix Proulx-Giraldeau
Jan M Skotheim
Paul François
Cell size is controlled to be within a specific range to support physiological function. To control their size, cells use diverse mechanisms… (see more) ranging from ‘sizers’, in which differences in cell size are compensated for in a single cell division cycle, to ‘adders’, in which a constant amount of cell growth occurs in each cell cycle. This diversity raises the question why a particular cell would implement one rather than another mechanism? To address this question, we performed a series of simulations evolving cell size control networks. The size control mechanism that evolved was influenced by both cell cycle structure and specific selection pressures. Moreover, evolved networks recapitulated known size control properties of naturally occurring networks. If the mechanism is based on a G1 size control and an S/G2/M timer, as found for budding yeast and some human cells, adders likely evolve. But, if the G1 phase is significantly longer than the S/G2/M phase, as is often the case in mammalian cells in vivo, sizers become more likely. Sizers also evolve when the cell cycle structure is inverted so that G1 is a timer, while S/G2/M performs size control, as is the case for the fission yeast S. pombe. For some size control networks, cell size consistently decreases in each cycle until a burst of cell cycle inhibitor drives an extended G1 phase much like the cell division cycle of the green algae Chlamydomonas. That these size control networks evolved such self-organized criticality shows how the evolution of complex systems can drive the emergence of critical processes.
2022-09-30
eLife (published)
doi.org
SPeCiaL: Self-Supervised Pretraining for Continual Learning
Lucas Caccia
Joelle Pineau
2022-09-28
Continual Semi-Supervised Learning (published)
doi.org
arxiv.org