Publications

Brain Age Prediction: Deep Models Need a Hand to Generalize
Reza Rajabli
Mahdie Soltaninejad
Vladimir S. Fonov
Danilo Bzdok
D. Louis Collins
Predicting brain age from T1‐weighted MRI is a promising marker for understanding brain aging and its associated conditions. While deep le… (voir plus)arning models have shown success in reducing the mean absolute error (MAE) of predicted brain age, concerns about robust and accurate generalization in new data limit their clinical applicability. The large number of trainable parameters, combined with limited medical imaging training data, contributes to this challenge, often resulting in a generalization gap where there is a significant discrepancy between model performance on training data versus unseen data. In this study, we assess a deep model, SFCN‐reg, based on the VGG‐16 architecture, and address the generalization gap through comprehensive preprocessing, extensive data augmentation, and model regularization. Using training data from the UK Biobank, we demonstrate substantial improvements in model performance. Specifically, our approach reduces the generalization MAE by 47% (from 5.25 to 2.79 years) in the Alzheimer's Disease Neuroimaging Initiative dataset and by 12% (from 4.35 to 3.75 years) in the Australian Imaging, Biomarker and Lifestyle dataset. Furthermore, we achieve up to 13% reduction in scan‐rescan error (from 0.80 to 0.70 years) while enhancing the model's robustness to registration errors. Feature importance maps highlight anatomical regions used to predict age. These results highlight the critical role of high‐quality preprocessing and robust training techniques in improving accuracy and narrowing the generalization gap, both necessary steps toward the clinical use of brain age prediction models. Our study makes valuable contributions to neuroimaging research by offering a potential pathway to improve the clinical applicability of deep learning models.
2025-07-15
Human Brain Mapping (publié)
doi.org
Longer scans boost prediction and cut costs in brain-wide association studies
Leon Qi Rong Ooi
Csaba Orban
Shaoshi Zhang
Thomas E. Nichols
Trevor Wei Kiat Tan
Ru Kong
Scott Marek
Nico U. F. Dosenbach
Timothy O. Laumann
Evan M. Gordon
Kwong Hsia Yap
Fang Ji
Joanna Su Xian Chong
Christopher Chen
Lijun An
Nicolai Franzmeier
Sebastian N. Roemer-Cassiano
Qingyu Hu
Jianxun Ren
Hesheng Liu … (voir 9 de plus)
Sidhant Chopra
Carrisa V. Cocuzza
Justin T. Baker
Juan Helen Zhou
Danilo Bzdok
Simon B. Eickhoff
Avram J. Holmes
B. T. Thomas Yeo
Clifford R. Jack Jr
A pervasive dilemma in brain-wide association studies (BWAS) is whether to prioritize functional MRI (fMRI) scan time or sample size. We der… (voir plus)ive a theoretical model showing that individual-level phenotypic prediction accuracy increases with sample size and total scan duration (sample size × scan time per participant). The model explains empirical prediction accuracies extremely well across 76 phenotypes from nine resting-fMRI and task-fMRI datasets (R2 = 0.89), spanning a wide range of scanners, acquisitions, racial groups, disorders and ages. For scans ≤20 mins, prediction accuracy increases linearly with the logarithm of total scan duration, suggesting interchangeability of sample size and scan time. However, sample size is ultimately more important than scan time in determining prediction accuracy. Nevertheless, when accounting for overhead costs associated with each participant (e.g., recruitment costs), to boost prediction accuracy, longer scans can yield substantial cost savings over larger sample size. To achieve high prediction performance, 10-min scans are highly cost inefficient. In most scenarios, the optimal scan time is ≥20 mins. On average, 30-min scans are the most cost-effective, yielding 22% cost savings over 10-min scans. Overshooting is cheaper than undershooting the optimal scan time, so we recommend aiming for ≥30 mins. Compared with resting-state whole-brain BWAS, the most cost-effective scan time is shorter for task-fMRI and longer for subcortical-cortical BWAS. Standard power calculations maximize sample size at the expense of scan time. Our study demonstrates that optimizing both sample size and scan time can boost prediction power while cutting costs. Our empirically informed reference is available for future study planning: WEB_APPLICATION_LINK
2025-07-15
Nature (publié)
doi.org
Exact risk curves of signSGD in High-Dimensions: quantifying preconditioning and noise-compression effects
Ke Liang Xiao
Noah Marshall
Atish Agarwala
Elliot Paquette
In recent years, signSGD has garnered interest as both a practical optimizer as well as a simple model to understand adaptive optimizers lik… (voir plus)e Adam. Though there is a general consensus that signSGD acts to precondition optimization and reshapes noise, quantitatively understanding these effects in theoretically solvable settings remains difficult. We present an analysis of signSGD in a high dimensional limit, and derive a limiting SDE and ODE to describe the risk. Using this framework we quantify four effects of signSGD: effective learning rate, noise compression, diagonal preconditioning, and gradient noise reshaping. Our analysis is consistent with experimental observations but moves beyond that by quantifying the dependence of these effects on the data and noise distributions. We conclude with a conjecture on how these results might be extended to Adam.
2025-07-14
International Conference on Machine Learning (Accept (poster))
doi.org
proceedings.mlr.press
In-context learning and Occam's razor
Eric Elmoznino
Tom Marty
Tejas Kasetty
Leo Gagnon
Sarthak Mittal
Mahan Fathi
Dhanya Sridhar
Guillaume Lajoie
A central goal of machine learning is generalization. While the No Free Lunch Theorem states that we cannot obtain theoretical guarantees fo… (voir plus)r generalization without further assumptions, in practice we observe that simple models which explain the training data generalize best: a principle called Occam's razor. Despite the need for simple models, most current approaches in machine learning only minimize the training error, and at best indirectly promote simplicity through regularization or architecture design. Here, we draw a connection between Occam's razor and in-context learning: an emergent ability of certain sequence models like Transformers to learn at inference time from past observations in a sequence. In particular, we show that the next-token prediction loss used to train in-context learners is directly equivalent to a data compression technique called prequential coding, and that minimizing this loss amounts to jointly minimizing both the training error and the complexity of the model that was implicitly learned from context. Our theory and the empirical experiments we use to support it not only provide a normative account of in-context learning, but also elucidate the shortcomings of current in-context learning methods, suggesting ways in which they can be improved. We make our code available at https://github.com/3rdCore/PrequentialCode.
2025-07-14
International Conference on Machine Learning (Accept (poster))
doi.org
proceedings.mlr.press
Leveraging Per-Instance Privacy for Machine Unlearning
Nazanin Mohammadi Sepahvand
Anvith Thudi
Berivan Isik
Ashmita Bhattacharyya
Nicolas Papernot
Eleni Triantafillou
Daniel M. Roy
Gintare Karolina Dziugaite
2025-07-14
International Conference on Machine Learning (Accept (poster))
doi.org
proceedings.mlr.press
Neural activity resolved in space and time through fusion of large-scale EEG and fMRI datasets.
Ian Charest
Peter Brotherwood
Mathias Salvas-Hébert
Kendrick Kay
Frédéric Gosselin
2025-07-14
Journal of Vision (publié)
doi.org
Relative Entropy Pathwise Policy Optimization
Claas Voelcker
Axel Brunnbauer
Marcel Hussing
Michal Nauman
Pieter Abbeel
Eric R. Eaton
Radu Grosu
Amir-massoud Farahmand
Igor Gilitschenski
Score-function policy gradients have delivered strong results in game-playing, robotics and language-model fine-tuning. Yet its high-varianc… (voir plus)e often undermines training stability. On the other hand, pathwise policy gradients alleviate the training variance, but are reliable only when driven by an accurate action-conditioned value function which is notoriously hard to train without relying on past off-policy data. In this paper, we discuss how to construct a value-gradient driven, on-policy algorithm that allow training Q-value models purely from on-policy data, unlocking the possibility of using pathwise policy updates in the context of on-policy learning. We show how to balance stochastic policies for exploration with constrained policy updates for stable training, and evaluate important architectural components that facilitate accurate value function learning. Building on these insights, we propose Relative Entropy Pathwise Policy Optimization (REPPO), an efficient on-policy algorithm that combines the sample-efficiency of pathwise policy gradients with the simplicity and minimal memory footprint of standard on-policy learning. We demonstrate that REPPO provides strong empirical performance at decreased sample requirements, wall-clock time, memory footprint as well as high hyperparameter robustness in a set of experiments on two standard GPU-parallelized benchmarks.
2025-07-14
ArXiv (prépublication)
doi.org
arxiv.org
STAGED: A Multi-Agent Neural Network for Learning Cellular Interaction Dynamics
João Felipe Rocha
Ke Xu
Xingzhi Sun
Ananya Krishna
Dhananjay Bhaskar
Blanche Mongeon
Morgan Craig
Mark B. Gerstein
Smita Krishnaswamy
The advent of single-cell technology has significantly improved our understanding of cellular states and subpopulations in various tissues u… (voir plus)nder normal and diseased conditions by employing data-driven approaches such as clustering and trajectory inference. However, these methods consider cells as independent data points of population distributions. With spatial transcriptomics, we can represent cellular organization, along with dynamic cell-cell interactions that lead to changes in cell state. Still, key computational advances are necessary to enable the data-driven learning of such complex interactive cellular dynamics. While agent-based modeling (ABM) provides a powerful framework, traditional approaches rely on handcrafted rules derived from domain knowledge rather than data-driven approaches. To address this, we introduce Spatio Temporal Agent-Based Graph Evolution Dynamics(STAGED) integrating ABM with deep learning to model intercellular communication, and its effect on the intracellular gene regulatory network. Using graph ODE networks (GDEs) with shared weights per cell type, our approach represents genes as vertices and interactions as directed edges, dynamically learning their strengths through a designed attention mechanism. Trained to match continuous trajectories of simulated as well as inferred trajectories from spatial transcriptomics data, the model captures both intercellular and intracellular interactions, enabling a more adaptive and accurate representation of cellular dynamics.
2025-07-14
ArXiv (prépublication)
doi.org
arxiv.org
Effects of a Virtual Reality Hypnosis Intervention on Chronic Pain: A User Experience and Proof-of-concept Study
Alexandra Chevestrier-Lefeuvre
Joséphine Guiné
Jade Véronneau
Julie Lebeau
Floriane Rousseaux
Audrey Laurin
Marie-Fania Simard
Nadia Godin
Karim Jerbi
Philippe Richebé
Mathieu Landry
Pierre Rainville
Valentyn Fournier
David Ogez
Abstract

Chronic pain is a significant public health issue in Canada, with approximately one in four Canadians ove… (voir plus)r the age of 15 living with this condition. Due to its impact on individuals—both physically and psychologically—and its financial burden on the healthcare system, it is crucial to develop cost-effective and efficient treatment methods. Hypnosis and virtual reality have emerged as promising solutions in this context. This study aims to evaluate the preliminary efficacy and feasibility of an intervention combining virtual reality and hypnosis. The study involved 30 patients with chronic pain who were invited to test a hypnosis application delivered through a virtual reality device. Levels of pain, anxiety, and relaxation were measured before and after the intervention, while satisfaction, cybersickness, and user experience were evaluated post-intervention. At the end of the intervention, participants were invited to participate in a semi-structured interview to provide feedback on their satisfaction with the experience. Participants reported high levels of satisfaction with the intervention, a positive user experience, and minimal symptoms of cybersickness. The intervention was effective in reducing anxiety (W = 173.5, p = .002) and pain (W = 253.5, p< .001) while significantly enhancing relaxation levels (W = 9.00, p< .001). This intervention demonstrated effectiveness in reducing pain and anxiety while improving relaxation levels among individuals with chronic pain, paving the way for further investigations of the involved mechanisms.

2025-07-13
Research Square (prépublication)
doi.org
Frequency enrichment of coding variants in a French-Canadian founder population and its implication for inflammatory bowel diseases
Claude Bhérer
Jean-Christophe Grenier
Justin Pelletier
Gabrielle Boucher
Genevieve Gagnon
Philippe Goyette
Dariel Ashton-Beaucage
Christine Stevens
Robert Battat
Alain Bitton
Philippe M Campeau
Catherine Laprise
Quebec IBD Genetics Consortium
Hailiang Huang
Mark Daly
Daniel Taliun
Julie G Hussin
Vincent Mooser
John D Rioux
The genetic features of founder populations with recent bottlenecks, causing some deleterious variants to rise to higher frequencies, can en… (voir plus)hance the power of rare variant association studies. French Canadians from Quebec represent a recent founder population with a particular disease heritage comprising more than 30 prevalent Mendelian conditions. Here, we characterize coding variation in this founder population using exome sequencing data from 2,820 French-Canadian participants - patients with inflammatory bowel diseases (IBD), parents and controls from the Quebec IBD cohort. We find that 18% of rare coding variants are 10-100 times more frequent than in non-Finnish Europeans (NFE). A total of 4,133 missense and loss-of-function variants were significantly enriched with a median 28-fold enrichment, revealing the potential for genotype-phenotype associations in this population. We describe significantly enriched pathogenic variants, including those known to account for the increased prevalence of rare diseases in FC compared to other European descent populations, such as Agenesis of corpus callosum and peripheral neuropathy (SLC12A6) and Leigh Syndrome French Canadian type (LRPPRC). Finally, we investigate whether rare protein-coding variants, enriched in French Canadians by the founder effect, contribute to the risk of IBD using trio and case/control cohorts. In addition to replicating associations in NOD2 and IL23R, we identified new candidate association signals, including enriched variants in SLC35E3, and ARSA. Our findings show that, even in well-characterized founder populations like the French Canadians, there remains untapped potential for genetic discovery, revealing both rare and complex disease risk factors through enriched coding variation.
2025-07-13
medRxiv (prépublication)
doi.org
Rational Retrieval Acts: Leveraging Pragmatic Reasoning to Improve Sparse Retrieval
Arthur Satouf
Gabriel Ben-Zenou
Benjamin Piwowarski
Habiboulaye Amadou-Boubacar
Pablo Piantanida
Current sparse neural information retrieval (IR) methods, and to a lesser extent more traditional models such as BM25, do not take into acco… (voir plus)unt the document collection and the complex interplay between different term weights when representing a single document. In this paper, we show how the Rational Speech Acts (RSA), a linguistics framework used to minimize the number of features to be communicated when identifying an object in a set, can be adapted to the IR case -- and in particular to the high number of potential features (here, tokens). RSA dynamically modulates token-document interactions by considering the influence of other documents in the dataset, better contrasting document representations. Experiments show that incorporating RSA consistently improves multiple sparse retrieval models and achieves state-of-the-art performance on out-of-domain datasets from the BEIR benchmark. https://github.com/arthur-75/Rational-Retrieval-Acts
2025-07-12
International ACM SIGIR Conference on Research and Development in Information Retrieval (publié)
doi.org
arxiv.org
Prompt4Trust: A Reinforcement Learning Prompt Augmentation Framework for Clinically-Aligned Confidence Calibration in Multimodal Large Language Models
Anita Kriz
Elizabeth Laura Janes
Xing Shen
Tal Arbel
Multimodal large language models (MLLMs) hold considerable promise for applications in healthcare. However, their deployment in safety-criti… (voir plus)cal settings is hindered by two key limitations: (i) sensitivity to prompt design, and (ii) a tendency to generate incorrect responses with high confidence. As clinicians may rely on a model's stated confidence to gauge the reliability of its predictions, it is especially important that when a model expresses high confidence, it is also highly accurate. We introduce Prompt4Trust, the first reinforcement learning (RL) framework for prompt augmentation targeting confidence calibration in MLLMs. A lightweight LLM is trained to produce context-aware auxiliary prompts that guide a downstream task MLLM to generate responses in which the expressed confidence more accurately reflects predictive accuracy. Unlike conventional calibration techniques, Prompt4Trust specifically prioritizes aspects of calibration most critical for safe and trustworthy clinical decision-making. Beyond improvements driven by this clinically motivated calibration objective, our proposed method also improves task accuracy, achieving state-of-the-art medical visual question answering (VQA) performance on the PMC-VQA benchmark, which is composed of multiple-choice questions spanning diverse medical imaging modalities. Moreover, our framework trained with a small downstream task MLLM showed promising zero-shot generalization to larger MLLMs in our experiments, suggesting the potential for scalable calibration without the associated computational costs. This work demonstrates the potential of automated yet human-aligned prompt engineering for improving the the trustworthiness of MLLMs in safety critical settings. Our codebase can be found at https://github.com/xingbpshen/prompt4trust.
2025-07-11
ArXiv (prépublication)
doi.org
arxiv.org