Publications

Sex Differences in P-Tau217, Tau Aggregation, and Cognitive Decline
Gillian Coughlan
Valentin Ourry
Diana Townsend
Hannah M Klinger
Jane A. Brown
Madison Cuppels
Tobey Betthauser
Rebecca Langhough
Karly Alex Cody
Mabel Seto
Colin Birkenbihl
Annie Li
Michelle Farrell
Emma G. Thibault
Pia Kivisäkk Webb
S. R. Arnold
Robert A. Rissman
Michael Properzi
Aaron Schultz
Keith Johnson … (voir 81 de plus)
Oliver Langford
Michael C. Donohue
Sylvia Villeneuve
Sterling C. Johnson
Hyun-Sik Yang
JoAnn E. Manson
Reisa Sperling
Rachel F. Buckley
Orest Hurko
Sanra E Black
Rachelle Doody
Murali Doraiswamy
Anthony Gamst
Jeffrey Kaye
Thomas Obisesan
Henry Rusinek
Doug Scharre
Reisa Sperling
Michael W Weiner
R. Green
Paul Aisen
Keith Johnson
Jason Karlawish
Kenneth Marek
Karen Holdridge
REMA RAMAN
R. Yaari
Cheryl Brown
John R. Sims
Robert A. Rissman
Michael C. Donohue
Maria Arampatizdou
Jeremy Pizzola
Mike Rafii
Clifford Jack Jr.
Marybeth Howlett
John Seibyl
Isabella Velon
Paula Cohen
Gustavo Jimenez-Maggiora
Marianne Manire
James B. Brewer
Paul Maruff
Mark Mintun
Alison Belsha
Jennifer Salazar
Cecily Jenkins
Vedeline Torreon
Renarda Jones
Sylvia Villeneuve
Judes Poirier
John C.S. Breitner
Mohamed Badawy
Sylvain Baillet
Andrée‐Ann Baril
Bellec Pierre
Véronique D. Bohbot
Danilo Bzdok
Mallar Chakravarty
D. Louis Collins
Mahsa Dadar
Simon Ducharme
Alan C. Evans
Claudine Gauthier
Maiya Geddes
Rick Hoge
Yasser Ituria-Medina
Maxime Montembeault
Gerhard Multhaup
Lisa-Marie Münter
Alexa Pichet Binette
Natasha Rajah
Pedro Rosa-Neto
Taylor W. Schmitz
Jean-Paul Soucy
Nathan R Spreng
Christine Tardif
Etienne Vachon-Presseau
Christian Bocti
Maxime Descoteaux
Pierre Bellec
Importance: Among individuals with high levels of amyloid-β (Aβ), women exhibit higher insoluble tau burden and accumulation than age-matc… (voir plus)hed men. It remains unclear whether this sex difference is influenced by soluble phosphorylated tau (p-tau), a biomarker that changes early in Alzheimer disease. Objective: To investigate whether sex and aggregated Aβ synergistically predict plasma phosphorylated tau 217 (p-tau217) levels and whether levels of p-tau217 predict cross-sectional and longitudinal tau aggregation in a sex-specific manner (as measured by positron emission tomography [PET]). Design, Setting, and Participants: This longitudinal study analyzed data between September 7, 2024, and October 29, 2025, from 1 clinical trial cohort and 4 observational study cohorts including men and women without cognitive impairment who had undergone multiple assessments via tau PET (18F-flortaucipir or 18F-MK-6240) and plasma p-tau217 assay at baseline. Cognitive performance was measured with the Preclinical Alzheimer Cognitive Composite. Data on cognitive performance were available from 3 of the 5 cohorts for a mean of 4.6 years (SD, 3.1 years). Across the 5 cohorts, the mean follow-up for tau PET was 3.6 years (SD, 1.7 years). Exposures: Self-reported sex (male or female), tau PET, and p-tau217 assay. Main Outcomes and Measures: The primary analyses used linear and mixed-effects models to assess baseline and longitudinal sex × p-tau217 interactions for 9 tau PET regions. The secondary analyses assessed sex × p-tau217 interactions for cognitive change using the Preclinical Alzheimer Cognitive Composite. Results: Across the 5 cohorts, there were a total of 1292 participants (63.6% women; mean age, 70.6 [SD, 6.4] years) with tau PET assessments. Compared with men, women had significantly higher baseline p-tau217 levels at higher aggregated Aβ Centiloid levels (β, -0.21 [95% CI, -0.37 to -0.05], P = .009; highest interaction was found in the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease/Longitudinal Evaluation of Amyloid Risk and Neurodegeneration [A4/LEARN] cohort). The sex × p-tau217 interactions at baseline were significant for 1 tau PET region in the Harvard Aging Brain Study (HABS) cohort, for 2 tau PET regions in the A4/LEARN cohort, for 6 tau PET regions in the Wisconsin Registry of Alzheimer's Prevention (WRAP) cohort, and for 4 tau PET regions in the Presymptomatic Evaluation of Experimental or Novel Treatments for Alzheimer's Disease (PREVENT-AD) cohort. Longitudinal interactions were significant for 4 tau PET regions in the A4/LEARN cohort, for 5 tau PET regions in both the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort and the WRAP cohort, and for 2 PET regions in both the HABS cohort and the PREVENT-AD cohort. Compared with men, women displayed greater tau deposition and accumulation at higher p-tau217 levels. Use of a secondary model showed women with higher p-tau217 levels also exhibited faster rates of cognitive decline relative to men in the both the WRAP cohort and the ADNI cohort. Conclusion and Relevance: These findings add to growing evidence that women have a differential tau response to Aβ that may emerge at the point of p-tau secretion. These findings have implications for the therapeutics and diagnostics of preclinical Alzheimer disease.
2026-02-15
JAMA Neurology (publié)
doi.org
ASTIH: A collection of axon and myelin segmentation datasets from multiple histology studies
Armand Collin
Mathieu Boudreau
Julien Cohen-Adad
Large-scale analysis of axon and myelin morphometry in nervous tissues is fundamental to neuroscience research, yet manual quantification re… (voir plus)mains a profound bottleneck, limiting the scale and efficiency of studies. To address this, we introduce the Axon Segmentation Training Initiative for Histology (ASTIH), a publicly accessible resource designed to propel the development and validation of automated histomorphometry tools. ASTIH comprises five meticulously curated datasets, standardized for machine learning applications, featuring over 69,000 manually segmented axon fibers. These datasets exhibit significant diversity, spanning three microscopy modalities (TEM, SEM, bright-field), three species (mouse, rat, rabbit), and three distinct anatomical regions (brain, spinal cord, peripheral nerves) with varying pixel resolutions (from ~0.2 to 0.002
2026-02-13
Medical Imaging with Deep Learning (poster)
openreview.net
openreview.net
PIKACHU: Prototypical In-context Knowledge Adaptation for Clinical Heterogeneous Usage
Amar Kumar
Zahra TehraniNasab
Emily Kaczmarek
Tal Arbel
Medical imaging systems increasingly rely on large vision language foundation models (VLFMs) trained on diverse biomedical corpora, yet thes… (voir plus)e models remain difficult to adapt to new clinical tasks without costly fine-tuning and large annotated datasets. We present PIKACHU (Prototypical In-Context Knowledge Adaptation for Clinical Heterogeneous Usage), a lightweight and generalizable framework that enables rapid few-shot adaptation of frozen medical FMs using only a handful of labeled examples. Unlike prior approaches that modify backbone weights or introduce heavy attention-based adapters, PIKACHU performs all task adaptation directly in the FM feature space through in-context prototypical reasoning. Given a small support set, the framework constructs class prototypes by averaging normalized embeddings from a frozen VLFM image encoder and performs prediction on query images using temperature-scaled cosine similarity. Only a single temperature parameter is learned. We evaluate PIKACHU across three heterogeneous medical imaging datasets - dermatological images (ISIC), Optical Coherence Tomography (OCT), and Diabetic Retinopathy (DR), using established vision models (SigLIP, PubMedCLIP, DinoV2, and ViT) as backbones. The proposed in-context learning (ICL) strategy consistently outperforms the baseline (zero-shot) approaches across all datasets and architectures, achieving substantial improvements in both accuracy and AUC. Notably, with PubMedCLIP as the backbone, PIKACHU achieves 0.69/0.76 (Acc./AUC) on the ISIC dataset, 0.72/0.78 on OCT, and 0.79/0.88 on DR, demonstrating robust generalization across diverse clinical imaging modalities. These results highlight the promise of feature-space in-context learning as efficient and deployable paradigms for test-time adaptation of foundation models, without the need for extensive retraining.
2026-02-13
Medical Imaging with Deep Learning (poster)
proceedings.mlr.press
proceedings.mlr.press
A flaw in using pretrained protein language models in protein–protein interaction inference models
Joseph Szymborski
Amin Emad
2026-02-12
Nature Machine Intelligence (publié)
doi.org
Motor Recovery After a Hemispherectomy: Review of Mechanisms and the Potential of Neuromodulation to Enhance Motor Outcomes
David Bergeron
Dorothy Barthélemy
Aristides Hadjinicolaou
Marco Bonizzato
Marina Martinez
Numa Dancause
Alexander G. Weil
In children with severe, refractory hemispheric epilepsy syndromes, the removal or disconnection of the diseased cortex on one hemisphere fr… (voir plus)om the rest of the brain (hemispherectomy) is a last-resort treatment to cure epilepsy. The removal or disconnection of the motor cortex expectedly leads to contralateral hemiparesis. Partial recovery of the leg or proximal arm may occur over time from the plasticity of alternate motor pathways, but finer hand movements generally do not recover. The advent of neuroprostheses delivering invasive or non-invasive stimulation at different levels of the motor pathways holds promise to enhance motor recovery after a neurologic injury. In this manuscript, we review the mechanisms of motor recovery after a hemispherectomy and discuss how emerging neuromodulation options could be used to improve function. We conclude that the most suitable neuromodulation options for short-term clinical trials are vagal nerve stimulation paired with rehabilitation, and tonic spinal cord stimulation (transcutaneous or with implanted electrodes). We also identify promising neuromodulation options that would require further preclinical investigation in animal models: subcortical deep brain stimulation (motor thalamus, contralateral dentate nucleus), brain-spine interfacing, and motor cortex stimulation. Altogether, this manuscript lays the theoretical foundations for the investigation of neuromodulation therapies to improve the motor outcomes of patients who underwent a hemispherectomy for refractory epilepsy.
2026-02-12
Journal of Child Neurology (publié)
doi.org
Multi-Agent Model-Based Reinforcement Learning with Joint State-Action Learned Embeddings
Zhizun Wang
David Meger
Learning to coordinate many agents in partially observable and highly dynamic environments requires both informative representations and dat… (voir plus)a-efficient training. To address this challenge, we present a novel model-based multi-agent reinforcement learning framework that unifies joint state-action representation learning with imaginative roll-outs. We design a world model trained with variational auto-encoders and augment the model using the state-action learned embedding (SALE). SALE is injected into both the imagination module that forecasts plausible future roll-outs and the joint agent network whose individual action values are combined through a mixing network to estimate the joint action-value function. By coupling imagined trajectories with SALE-based action values, the agents acquire a richer understanding of how their choices influence collective outcomes, leading to improved long-term planning and optimization under limited real-environment interactions. Empirical studies on well-established multi-agent benchmarks, including StarCraft II Micro-Management, Multi-Agent MuJoCo, and Level-Based Foraging challenges, demonstrate consistent gains of our method over baseline algorithms and highlight the effectiveness of joint state-action learned embeddings within a multi-agent model-based paradigm.
2026-02-12
arXiv (prépublication)
doi.org
arxiv.org
Predicting viral respiratory tract infections using wearable biosensor monitoring during 3-minute constant rate stair stepping tests
Oussama Jlassi
Amir Hadid
Emily G. McDonald
Qianggang Ding
Christopher Phillipp
Audrey Trottier
Matthew Pellan Cheng
Jesse Papenburg
Michael Libman
Dennis Jensen
Philippe C. Dixon
Background Viral Respiratory Tract Infections (VRTIs) are a major public health threat. Early detection and preventive measures are key to c… (voir plus)ontrolling their spread. Current machine learning approaches often depend on symptom onset, costly equipment, trained personnel, and slow results. This study aims to evaluate whether a machine learning algorithm using physiological data from wearable biosensors during a constant-rate stair-stepping task (3-min test, 2-min recovery) can predict inflammation levels, and to identify the most predictive indicators of VRTI. Methods 55 Healthy participants (27 males and 28 females) aged 18–59 years, were recruited and inoculated with a live influenza vaccine to induce an immune response, assessed via changes in circulating inflammatory biomarkers. Physiological markers, including breathing rate and heart rate, during a series of clinically controlled stair tests, were monitored by a wearable biosensor. These data were collected to develop a prediction model using gradient-boosting machine learning algorithms combined with hyperparameter tuning and a leave-one-subject-out method to train the models. Results The study developed a predictive model that accurately estimates inflammation levels in individuals. Features from heart rate variability (HRV) showed the greatest potential, with 70% sensitivity and 77% specificity, and physiological markers from controlled stair tests correlated with VRTI-related inflammatory responses. Impact The prediction model linked to stair-stepping tests offers clinicians and the public a tool for self-monitoring and early intervention. Using machine learning and physiological markers, especially HRV features, it can help guide timely treatments and reduce the impact of future outbreaks.
2026-02-12
Frontiers in Sensors (publié)
doi.org
AI Researchers' Views on Automating AI R&D and Intelligence Explosions
Severin Field
Raymond Douglas
David Krueger
Many leading AI researchers expect AI development to exceed the transformative impact of all previous technological revolutions. This belief… (voir plus) is based on the idea that AI will be able to automate the process of AI research itself, leading to a positive feedback loop. In August and September of 2025, we interviewed 25 leading researchers from frontier AI labs and academia, including participants from Google DeepMind, OpenAI, Anthropic, Meta, UC Berkeley, Princeton, and Stanford to understand researcher perspectives on these scenarios. Though AI systems have not yet been able to recursively improve, 20 of the 25 researchers interviewed identified automating AI research as one of the most severe and urgent AI risks. Participants converged on predictions that AI agents will become more capable at coding, math and eventually AI development, gradually transitioning from `assistants'or `tools'to `autonomous AI developers,'after which point, predictions diverge. While researchers agreed upon the possibility of recursive improvement, they disagreed on basic questions of timelines or appropriate governance mechanisms. For example, an epistemic divide emerged between frontier lab researchers and academic researchers, the latter of which expressed more skepticism about explosive growth scenarios. Additionally, 17/25 participants expected AI systems with advanced coding or R&D capabilities to be increasingly reserved for internal use at AI companies or governments, unseen by the public. Participants were split as to whether setting regulatory ``red lines"was a good idea, though almost all favored transparency-based mitigations.
2026-02-12
arXiv (prépublication)
doi.org
arxiv.org
Pregnancy AI: Development and Internal Validation of an Artificial Intelligence Tool to Predict Live Births in ICSI and IVF Cycles Using Clinical Features and Embryo Images
Jaume Minano Masip
Penelope Borduas
Isaac-Jacques Kadoch
Simon Phillips
Doina Precup
Daniel Dufort
2026-02-11
Medicina (publié)
doi.org
Stabilizing Native Low-Rank LLM Pretraining
Paul Janson
Edouard Oyallon
Eugene Belilovsky
Foundation models have achieved remarkable success, yet their growing parameter counts pose significant computational and memory challenges.… (voir plus) Low-rank factorization offers a promising route to reduce training and inference costs, but the community lacks a stable recipe for training models from scratch using exclusively low-rank weights while matching the performance of the dense model. We demonstrate that Large Language Models (LLMs) can be trained from scratch using exclusively low-rank factorized weights for all non-embedding matrices without auxiliary"full-rank"guidance required by prior methods. While native low-rank training often suffers from instability and loss spikes, we identify uncontrolled growth in the spectral norm (largest singular value) of the weight matrix update as the dominant factor. To address this, we introduce Spectron: Spectral renormalization with orthogonalization, which dynamically bounds the resultant weight updates based on the current spectral norms of the factors. Our method enables stable, end-to-end factorized training with negligible overhead. Finally, we establish compute-optimal scaling laws for natively low-rank transformers, demonstrating predictable power-law behavior and improved inference efficiency relative to dense models.
2026-02-11
ArXiv (prépublication)
arxiv.org
arxiv.org
Affordances Enable Partial World Modeling with LLMs
Khimya Khetarpal
Gheorghe Comanici
Jonathan Richens
Jeremy Shar
Fei Xia
Laurent Orseau
Aleksandra Faust
Doina Precup
2026-02-10
ArXiv (prépublication)
arxiv.org
arxiv.org
Improving the Robustness of Large Language Models for Code Tasks via Fine-tuning with Perturbed Data
Yang Liu
Armstrong Foundjem
Xingfang Wu
Heng Li
Foutse Khomh
Context: In the fast-paced evolution of software development, Large Language Models (LLMs) have become indispensable tools for tasks such as… (voir plus) code generation, completion, analysis, and bug fixing. Ensuring the robustness of these models against potential vulnerabilities from handling diverse inputs is critical, as variations in input can lead to incorrect or insecure code outputs. Objective: This work aims to improve the robustness of LLMs for coding-related tasks against potential adversarial inputs. Specifically, we investigate how fine-tuning LLMs with perturbed datasets impacts their robustness against input perturbations. Method: We systematically evaluated LLM robustness by fine-tuning models using datasets perturbed at character-level, word-level, and sentence-level, comparing results against base models and models fine-tuned on unperturbed datasets. Results: Fine-tuning LLMs with perturbed datasets significantly improves model robustness (RD usually drops around 4\% - 6\%), especially for models with relatively weak robustness. However, this fine-tuning process typically results in a slight performance decrease (pass@1 usually drops around 1\% - 3\%) compared to fine-tuning with unperturbed datasets, although occasional performance improvements are observed. Conclusion \&Implications: Fine-tuning LLMs for coding tasks with perturbed data effectively enhances their robustness at the cost of a minor performance reduction, emphasizing the importance of balancing the robustness and performance of LLMs for coding applications.
2026-02-10
ArXiv (prépublication)
arxiv.org
arxiv.org