Publications

Learning Shared Neural Manifolds from Multi-Subject FMRI Data
Jessie Huang
Je-chun Huang
Erica Lindsey Busch
Tom Wallenstein
Michal Gerasimiuk
Andrew Benz
Guillaume Lajoie
Guy Wolf
Nicholas Turk-Browne
Smita Krishnaswamy
Functional magnetic resonance imaging (fMRI) data is collected in millions of noisy, redundant dimensions. To understand how different brain… (voir plus)s process the same stimulus, we aim to denoise the fMRI signal via a meaningful embedding space that captures the data's intrinsic structure as shared across brains. We assume that stimulus-driven responses share latent features common across subjects that are jointly discoverable. Previous approaches to this problem have relied on linear methods like principal component analysis and shared response modeling. We propose a neural network called MRMD-AE (manifold-regularized multiple- decoder, autoencoder) that learns a common embedding from multi-subject fMRI data while retaining the ability to decode individual responses. Our latent common space represents an extensible manifold (where untrained data can be mapped) and improves classification accuracy of stimulus features of unseen timepoints, as well as cross-subject translation of fMRI signals.
2022-08-22
2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP) (publié)
doi.org
openreview.net
Proteogenomics and Differential Ion Mobility Enable the Exploration of the Mutational Landscape in Colon Cancer Cells
Zhaoguan Wu
Eric Bonneil
Michael Belford
Cornelia Boeser
Maria-Virginia Ruiz Cuevas
Sébastien Lemieux
Jean-Jacques Dunyach
Pierre Thibault
2022-08-22
Analytical Chemistry (publié)
doi.org
Stratifying the autistic phenotype using electrophysiological indices of social perception
Luke Mason
Carolin Moessnang
Christopher H. Chatham
Lindsay Ham
Julian Tillmann
Guillaume Dumas
Claire Ellis
Claire Leblond
Freddy Cliquet
Thomas Bourgeron
Christian Beckmann
Tony Charman
Beth Oakley
Tobias Banaschewski
Andreas Meyer-Lindenberg
Simon Baron-Cohen
Sven Bölte
Jan K. Buitelaar
Sarah Durston
Eva Loth … (voir 7 de plus)
Bob Oranje
Antonio Persico
Flavio Dell’Acqua
Christine Ecker
Mark Johnson
Declan Murphy
Emily J. H. Jones
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in social communication, but also great heter… (voir plus)ogeneity. To offer individualized medicine approaches, we need to better target interventions by stratifying autistic people into subgroups with different biological profiles and/or prognoses. We sought to validate neural responses to faces as a potential stratification factor in ASD by measuring neural (electroencephalography) responses to faces (critical in social interaction) in N = 436 children and adults with and without ASD. The speed of early-stage face processing (N170 latency) was on average slower in ASD than in age-matched controls. In addition, N170 latency was associated with responses to faces in the fusiform gyrus, measured with functional magnetic resonance imaging, and polygenic scores for ASD. Within the ASD group, N170 latency predicted change in adaptive socialization skills over an 18-month follow-up period; data-driven clustering identified a subgroup with slower brain responses and poor social prognosis. Use of a distributional data-driven cutoff was associated with predicted improvements of power in simulated clinical trials targeting social functioning. Together, the data provide converging evidence for the utility of the N170 as a stratification factor to identify biologically and prognostically defined subgroups in ASD. Description N170 latency to faces relates to fusiform activity and ASD genetics, predicts social prognosis, and could improve power in clinical trials. Exploiting face processing in patients with ASD The heterogeneity observed in patients with autism spectrum disorder (ASD) highlights the need for better patient stratification methods. Here, Mason et al. evaluated the use of the speed of early-stage face processing (N170 latency) for patient stratification and prognosis in subjects with ASD and age-matched healthy individuals. N170 latency was slower in individuals with ASD and correlated with response to faces measured with fMRI and with polygenic risk score. Among subjects with ASD, the N170 values stratified patients according to socialization prognosis and improved power in a simulated clinical trial. The results suggest that including N170 evaluation in patient stratification might help the design and development of patient-specific therapies for ASD.
2022-08-17
Science Translational Medicine (publié)
doi.org
Two types of human TCR differentially regulate reactivity to self and non-self antigens
Assya Trofimov
Philippe Brouillard
Jean-David Larouche
Jonathan Séguin
Jean-Philippe Laverdure
Ann Brasey
Grégory Ehx
Denis-Claude Roy
Lambert Busque
Silvy Lachance
Sébastien Lemieux
Claude Perreault
2022-08-17
iScience (publié)
doi.org
Pandemic policy assessment by artificial intelligence
Sirui Song
Xue (Steve) Liu
Yong Li
Yang Yu
2022-08-16
Scientific Reports (publié)
doi.org
Pandemic policy assessment by artificial intelligence
Sirui Song
Xue (Steve) Liu
Yong Li
Yang Yu
2022-08-16
Scientific Reports (published)
doi.org
High Fidelity Visualization of What Your Self-Supervised Representation Knows About
Florian Bordes
Randall Balestriero
Pascal Vincent
Discovering what is learned by neural networks remains a challenge. In self-supervised learning, classification is the most common task used… (voir plus) to evaluate how good a representation is. However, relying only on such downstream task can limit our understanding of what information is retained in the representation of a given input. In this work, we showcase the use of a Representation Conditional Diffusion Model (RCDM) to visualize in data space the representations learned by self-supervised models. The use of RCDM is motivated by its ability to generate high-quality samples -- on par with state-of-the-art generative models -- while ensuring that the representations of those samples are faithful i.e. close to the one used for conditioning. By using RCDM to analyze self-supervised models, we are able to clearly show visually that i) SSL (backbone) representation are not invariant to the data augmentations they were trained with -- thus debunking an often restated but mistaken belief; ii) SSL post-projector embeddings appear indeed invariant to these data augmentation, along with many other data symmetries; iii) SSL representations appear more robust to small adversarial perturbation of their inputs than representations trained in a supervised manner; and iv) that SSL-trained representations exhibit an inherent structure that can be explored thanks to RCDM visualization and enables image manipulation.
2022-08-15
TMLR (accepté)
openreview.net
openreview.net
Automatic Phenotyping by a Seed-guided Topic Model
Ziyang Song
Yuanyi Hu
Aman Verma
David Buckeridge
Yue Li
Electronic health records (EHRs) provide rich clinical information and the opportunities to extract epidemiological patterns to understand a… (voir plus)nd predict patient disease risks with suitable machine learning methods such as topic models. However, existing topic models do not generate identifiable topics each predicting a unique phenotype. One promising direction is to use known phenotype concepts to guide topic inference. We present a seed-guided Bayesian topic model called MixEHR-Seed with 3 contributions: (1) for each phenotype, we infer a dual-form of topic distribution: a seed-topic distribution over a small set of key EHR codes and a regular topic distribution over the entire EHR vocabulary; (2) we model age-dependent disease progression as Markovian dynamic topic priors; (3) we infer seed-guided multi-modal topics over distinct EHR data types. For inference, we developed a variational inference algorithm. Using MixEHR-Seed, we inferred 1569 PheCode-guided phenotype topics from an EHR database in Quebec, Canada covering 1.3 million patients for up to 20-year follow-up with 122 million records for 8539 and 1126 unique diagnostic and drug codes, respectively. We observed (1) accurate phenotype prediction by the guided topics, (2) clinically relevant PheCode-guided disease topics, (3) meaningful age-dependent disease prevalence. Source code is available at GitHub: https://github.com/li-lab-mcgill/MixEHR-Seed.
2022-08-14
Knowledge Discovery and Data Mining (publié)
doi.org
TITRATED: Learned Human Driving Behavior without Infractions via Amortized Inference
Vasileios Lioutas
Adam Ścibior
Frank Wood
2022-08-12
TMLR (accepté)
openreview.net
openreview.net
Heatmap Regression for Lesion Detection using Pointwise Annotations
Chelsea Myers-Colet
Julien Schroeter
Douglas Arnold
Tal Arbel
In many clinical contexts, detecting all lesions is imperative for evaluating disease activity. Standard approaches pose lesion detection as… (voir plus) a segmentation problem despite the time-consuming nature of acquiring segmentation labels. In this paper, we present a lesion detection method which relies only on point labels. Our model, which is trained via heatmap regression, can detect a variable number of lesions in a probabilistic manner. In fact, our proposed post-processing method offers a reliable way of directly estimating the lesion existence uncertainty. Experimental results on Gad lesion detection show our point-based method performs competitively compared to training on expensive segmentation labels. Finally, our detection model provides a suitable pre-training for segmentation. When fine-tuning on only 17 segmentation samples, we achieve comparable performance to training with the full dataset.
2022-08-11
ArXiv (prépublication)
doi.org
arxiv.org
RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
Thibaud Godon
Pier-Luc Plante
Baptiste Bauvin
Élina Francovic-Fontaine
Alexandre Drouin
Jacques Corbeil
Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine. The high dimensio… (voir plus)nality of metabolomics data challenges learning algorithms in terms of scalability and generalization. Most learning algorithms do not produce interpretable models -- Method: We propose an ensemble learning algorithm based on conjunctions or disjunctions of decision rules. -- Results : Applications on metabolomics data shows that it produces models that achieves high predictive performances. The interpretability of the models makes them useful for biomarker discovery and patterns discovery in high dimensional data.
2022-08-11
ArXiv (prépublication)
doi.org
arxiv.org
Learning to Improve Code Efficiency
Binghong Chen
Danny Tarlow
Kevin Swersky
Martin Maas
Pablo Heiber
Ashish V Naik
Milad Hashemi
Parthasarathy Ranganathan
2022-08-09
ArXiv (prépublication)
doi.org
openreview.net