Publications

Segmentation of spinal rootlets across MRI contrasts with RootletSeg.
Katerina Krejci
Jiri Chmelik
Sandrine Bédard
Falk Eippert
Ulrike Horn
Virginie Callot
Julien Cohen-Adad
Jan Valosek
Segmentation of spinal nerve rootlets is relevant for spinal level estimation, lesion classification, neuromodulation therapy, and group-lev… (see more)el analyses. The aim of this study was to develop a deep learning method for the automatic segmentation of C2-T1 dorsal and ventral spinal nerve rootlets on various MRI scans. The study included MRI scans from two open-access and one private dataset, consisting of 3D isotropic 3T turbo spin echo T2-weighted (T2w) and 7T MP2RAGE (T1-weighted [T1w] INV1 and INV2, and UNIT1) MRI scans. A deep learning model, RootletSeg, was developed on 93 MRI scans from 50 healthy adults (mean age, 28.70 years ± 6.53 [SD]; 28 [56%] males, 22 [44%] females) and achieved a mean ± SD Dice score of 0.67 ± 0.09 for T1w-INV2, 0.65 ± 0.11 for UNIT1, 0.64 ± 0.08 for T2w, and 0.62 ± 0.10 for T1w-INV1 contrasts. RootletSeg accurately segmented C2-T1 spinal rootlets across MRI contrasts, enabling the determination of spinal levels directly from MRI scans. The method is open-source and can be used for a variety of downstream analyses.
2026-05-01
Scientific Reports (published)
doi.org
Automatic multiple sclerosis lesion segmentation in the spinal cord using 3 T and 7 T MP2RAGE images
Nilser Laines Medina
Samira Mchinda
Benoit Testud
Sarah Demortière
Emanuele Pravatà
Govind Nair
Daniel S. Reich
Cristina Granziera
Charidimos Tsagkas
Virginie Callot
Julien Cohen-Adad
2026-04-30
Multiple Sclerosis and Related Disorders (published)
doi.org
ControBench: An Interaction-Aware Benchmark for Controversial Discourse Analysis on Social Networks
Ta Thanh Thuy
Jiaqi Zhu
Xuan Liu
Lin Shang
Reihaneh Rabbany
Guillaume Rabusseau
Lihui Chen
Zheng Yilun
Sitao Luan
Understanding how people argue across ideological divides online is important for studying political polarization, misinformation, and conte… (see more)nt moderation. Existing datasets capture only part of this problem: some preserve text but ignore interaction structure, some model structure without rich semantics, and others represent conversations without stable user-level ideological identity. We introduce ControBench, a benchmark for controversial discourse analysis that combines heterogeneous social interaction graphs with rich textual semantics. Built from Reddit discussions on three topics, Trump, abortion, and religion, ControBench contains 7,370 users, 1,783 posts, and 26,525 interactions. The graph contains user and post nodes connected by semantically enriched edges; in particular, user-comment-user edges encode both a reply and the parent comment that it responds to, preserving local argumentative context. User labels are derived from self-declared Reddit flairs, providing a scalable proxy for ideological identity without manual annotation. The resulting datasets exhibit low or negative adjusted homophily (Trump: -0.77, Abortion: 0.06, Religion: 0.04), reflecting the cross-cutting structure of real-world debate. We evaluate graph neural networks, pretrained language models, and large language models on ControBench and observe distinct performance patterns across topics and model families, especially when ideological boundaries are ambiguous. These results position ControBench as a challenging and realistic benchmark for controversial discourse analysis.
2026-04-30
arXiv (preprint)
doi.org
arxiv.org
Oscillatory co-expression of HES1 and HES5 Enables a hybrid state in a cross-repressive transcription factor regulatory motif.
Veronica Biga
Anzy Miller
Anoushka Kamath
Robert Lea
Ying Q P Mak
Antony Adamson
Elli Marinopoulou
Paul François
Nancy Papalopulu
Cerys Manning
Many cell fate decisions in the developing neural tube are directed by cross-repressive transcription factor (TF) motifs that generate bista… (see more)bility, such that cells express one TF but not both. Hybrid states in which cells express both cross-repressing fate determinants have been observed, but how these arise or persist remains unclear. Here, we focus on HES1 and HES5, auto-repressive, oscillatory TFs that regulate neural progenitor maintenance and are expressed in adjacent dorsoventral progenitor domains in the developing spinal cord. Knockdown experiments demonstrate that HES1 and HES5 are cross-repressing in mouse spinal cord neural progenitors, and live-cell imaging in vitro shows that they can be co-expressed, defining a hybrid state. In this state, HES co-oscillate in-phase within single cells. Computational modelling indicates that modulation of cross-repression strength or relative TF abundance destabilises this state, driving resolution towards a single oscillatory HES TF. This is consistent with in vivo analysis showing transient HES1/HES5 co-expression followed by progressive restriction to a single TF oscillator. Our findings suggest that oscillatory expression enables co-existence of cross-repressing TFs, allowing hybrid states within a developmental bistable motif.
2026-04-30
Development (published)
doi.org
Physics-informed cross-coupled information flow modeling for spatiotemporal dynamical systems
Hangyi Yu
Hanqing Zhao
Yu Zhang
Lianlei Lin
Zongwei Zhang
Sheng Gao
Junkai Wang
2026-04-30
Information Sciences (published)
doi.org
Position: agentic AI orchestration should be Bayes-consistent
Theodore Papamarkou
Pierre Alquier
Matthias Bauer
Wray Buntine
A. Davison
Gintare Karolina Dziugaite
Maurizio Filippone
Andrew Y. K. Foong
Vincent Fortuin
Dimitris Fouskakis
Jes Frellsen
Eyke Hüllermeier
Theofanis Karaletsos
Mohammad Emtiyaz Khan
Nikita Kotelevskii
Salem Lahlou
Yingzhen Li
Fang Liu
Clare Lyle
Thomas Möllenhoff … (see 10 more)
Konstantina Palla
Maxim Panov
Yusuf Sale
Kajetan Schweighofer
Artem Shelmanov
Siddharth Swaroop
Martin Trapp
Willem Waegeman
Andrew Gordon Wilson
Alexey Zaytsev
LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example… (see more), which tool to call, which expert to consult, or how many resources to invest. While the usefulness and feasibility of Bayesian approaches remain unclear for LLM inference, this position paper argues that the control layer of an agentic AI system (that orchestrates LLMs and tools) is a clear case where Bayesian principles should shine. Bayesian decision theory provides a framework for agentic systems that can help to maintain beliefs over task-relevant latent quantities, to update these beliefs from observed agentic and human-AI interactions, and to choose actions. Making LLMs themselves explicitly Bayesian belief-updating engines remains computationally intensive and conceptually nontrivial as a general modeling target. In contrast, this paper argues that coherent decision-making requires Bayesian principles at the orchestration level of the agentic system, not necessarily the LLM agent parameters. This paper articulates practical properties for Bayesian control that fit modern agentic AI systems and human-AI collaboration, and provides concrete examples and design patterns to illustrate how calibrated beliefs and utility-aware policies can improve agentic AI orchestration.
2026-04-30
arXiv (preprint)
doi.org
arxiv.org
Profiling the Cell-Type Specific Effects of Psilocybin in Medial Prefrontal Cortexh
Heike Schuler
Delong Zhou
Chloé Savignac
Vedrana Cvetkovska
Yiu-Chung Tse
Juliet Meccia
Danilo Bzdok
Rosemary C. Bagot
2026-04-30
Biological Psychiatry (published)
doi.org
Refining the construct of direct verbal suggestibility: Evidence for a hybrid dimensional–typological latent structure
Jérémy Brunel
Audrey Vanhaudenhuyse
Julie Delage
Karim Jerbi CoCo Lab
Pierre Rainville
David Ogez
Mathieu Landry
2026-04-30
Personality and Individual Differences (published)
doi.org
SCEIMA: Social Coordination Evaluation through Integrated Model Analysis
Bavo Van Kerrebroeck
Caroline Palmėr
Guillaume Dumas
Alexander P. Demos
Computational models are increasingly used as interactive partners in studies of human coordination, yet it remains unclear whether observed… (see more) differences in human behavior reflect properties of the models themselves, changes in human behavior elicited by such artificial partners, or both. We introduce SCEIMA (Social Coordination Evaluation through Integrated Model Analysis), a two-stage framework designed to disentangle human-specific, model-specific, and interaction-driven contributions to coordination in human–machine interaction paradigms. In the empirical stage, human participants perform a coordination task with both human partners and computational models, establishing reference human–human and human–model interaction patterns. In the analytical stage, the same models are paired with one another and optimized through simulations to reproduce empirical coordination metrics. Comparing human–human, human–model, and simulated model–model interactions reveals whether coordination differences arise from intrinsic model dynamics, from human adaptation to artificial partners, or from their interaction. SCEIMA treats computational models as contrastive instruments whose capacity to elicit and reproduce human behavior can be systematically evaluated. We illustrate the framework with two distinct case-studies, a sensorimotor synchronization task and a conversational turn-taking task, showing how distinct outcome patterns diagnose the sources of coordination differences. By providing a principled methodological framework for evaluating interactive computational models, SCEIMA improves interpretability in human–machine interaction research and informs the design of artificial agents that coordinate with humans more naturally and responsively.
2026-04-30
Computers in Human Behavior Reports (published)
doi.org
The ethical impasse of current consciousness science
Vincent Taschereau-Dumouchel
Jun Seo Hwang
Hakwan Lau
Joseph E. LeDoux
2026-04-30
Neuron (published)
doi.org
Training a neural network to rapidly identify candidate gravitational-wave events in the lower mass gap
Nayyer Raza
Man Leong Chan
Daryl Haggard
Ashish Mahabal
Jess McIver
Audrey Durand
Alexandre Larouche
Hadi Moazen
The physics governing the boundary between the most massive neutron stars (NSs) and the least massive black holes (BHs) is currently uncerta… (see more)in, but could potentially be constrained with new observations. While NSs have been observed with masses up to
2026-04-30
arXiv (preprint)
doi.org
arxiv.org
Large Language Models Are Good Term Extractors: A Systematic Evaluation
Ayla Rigouts Terryn
2026-04-29
Language Resources and Evaluation Conference (published)
doi.org