Publications

Online HD-tRNS over the right temporoparietal junction modulates social inference but not motor coordination
Quentin Moreau
Vincent Chamberland
Lisane Moses
Gabriela Milanova
Guillaume Dumas
Social interactions are fundamental to human cognition, with the right temporoparietal junction (rTPJ) playing a key role in integrating mot… (see more)or coordination and social inference. While transcranial random noise stimulation (tRNS) is a promising technique for modulating cortical excitability in real time, its effect on dynamic social processes remains largely unexplored. This study applied high-definition tRNS (HD-tRNS) over the rTPJ during an interactive task to modulate motor coordination and social inference. Eighty neurotypical adults (49 female) were equally distributed across two experiments: Experiment 1, a block design with randomized active and sham stimulation blocks; or Experiment 2, a trial-by-trial design with intermixed stimulation protocols. Participants performed a coordination task with a covert virtual partner programmed to behave cooperatively or competitively. Kinematic data and self-reported attributions of humanness and cooperativeness were analyzed. The results showed that HD-tRNS over the rTPJ did not affect motor coordination or overall task performance in either experiment. However, in Experiment 1, active stimulation progressively reduced attributed humanness and cooperativeness toward the competitive virtual partner, suggesting enhanced detection of antagonistic intent. This gradual modulation of social inference was absent in Experiment 2, where frequent protocol switching likely disrupted the buildup of stimulation effects. Together, these findings highlight the rTPJ's causal role in self–other distinction, underscore the importance of stimulation protocol design in shaping social cognition, and support the exploration of targeted neuromodulation in clinical and developmental populations with atypical social cognition.
2025-04-18
eNeuro (published)
doi.org
DTractor enhances cell type deconvolution in spatial transcriptomics by integrating deep neural networks, transfer learning, and matrix factorization
Yong Jin Kweon
Chenyu Liu
Gregory Fonseca
Jun Ding
Spatial transcriptomics (ST) captures gene expression with spatial context but lacks single-cell resolution. Single-cell RNA sequencing (scR… (see more)NA-seq) offers high-resolution profiles without spatial information. Accurate spot-level decomposition requires effective integration of both. We present DTractor, a deep learning-based framework that improves cell-type deconvolution in ST data through spatial constraints and transfer learning. DTractor achieves dual utilization of scRNA-seq reference data by incorporating both a cell-type-specific gene expression matrix and learned latent embeddings into a unified matrix factorization model. This joint modeling enables accurate estimation of cell-type proportions and cell-type-resolved gene expression within each spatial spot, while preserving biological and spatial coherence. DTractor further applies spatial regularization to maintain local tissue structure. Across multiple ST platforms and tissue types, DTractor demonstrates improved decomposition accuracy, robustness, and interpretability compared to existing methods. The results from DTractor support downstream applications such as spatial domain analysis and the study of spatially organized cellular behaviors.
2025-04-17
bioRxiv (preprint)
doi.org
GeoLS: an Intensity-based, Geodesic Soft Labeling for Image Segmentation
Sukesh Adiga Vasudeva
Jose Dolz
Hervé Lombaert
Soft-label assignments have emerged as prominent strategies in training dense prediction problems, such as image segmentation. These approac… (see more)hes mitigate the limitations of hard labels, such as inter-class relationships in the data and spatial relationships between a given pixel and its neighbors. Nevertheless, most existing methods rely only on ground-truth masks and ignore the underlying image context associated with each label. For instance, image intensities convey information that could potentially clear ambiguities in the annotation. This paper, therefore, proposes a Geodesic Label Smoothing (GeoLS) approach that incorporates image intensity information within the soft labeling process. Specifically, we leverage the geodesic distance transform to capture the intensity variations between pixels. The generated maps geodesically modify the hard labels to obtain new intensity-based soft labels. The resulting geodesic soft labels better model spatial and class-wise relationships as they capture the variations of image gradients across classes and anatomy. The benefits of our intensity-based geodesic soft labels are assessed on three diverse sets of publicly accessible segmentation datasets. Our experimental results show that the proposed method consistently improves the segmentation accuracy compared to state-of-the-art soft-labeling techniques in terms of the Dice similarity and Hausdorff distance.
2025-04-17
Machine Learning for Biomedical Imaging (published)
doi.org
Design Editing for Offline Model-based Optimization
Ye Yuan
Youyuan Zhang
Can (Sam) Chen
Haolun Wu
Zixuan (Melody) Li
Jianmo Li
James J. Clark
Xue Liu
Offline model-based optimization (MBO) aims to maximize a black-box objective function using only an offline dataset of designs and scores. … (see more)These tasks span various domains, such as robotics, material design, and protein and molecular engineering. A common approach involves training a surrogate model using existing designs and their corresponding scores, and then generating new designs through gradient-based updates with respect to the surrogate model. This method suffers from the out-of-distribution issue, where the surrogate model may erroneously predict high scores for unseen designs. To address this challenge, we introduce a novel method, Design Editing for Offline Model-based Optimization (DEMO), which leverages a diffusion prior to calibrate overly optimized designs. DEMO first generates pseudo design candidates by performing gradient ascent with respect to a surrogate model. While these pseudo design candidates contain information beyond the offline dataset, they might be invalid or have erroneously high predicted scores. Therefore, to address this challenge while utilizing the information provided by pseudo design candidates, we propose an editing process to refine these pseudo design candidates. We introduce noise to the pseudo design candidates and subsequently denoise them with a diffusion prior trained on the offline dataset, ensuring they align with the distribution of valid designs. Empirical evaluations on seven offline MBO tasks show that, with properly tuned hyperparameters, DEMOs score is competitive with the best previously reported scores in the literature.
2025-04-16
Transactions on Machine Learning Research (accepted)
doi.org
openreview.net
Multimodal population study reveals the neurobiological underpinnings of chronotype
Le Zhou
Karin Saltoun
Julie Carrier
Kai-Florian Storch
Robin I. M. Dunbar
Danilo Bzdok
2025-04-16
Nature Human Behaviour (unknown)
doi.org
Sliced-Wasserstein Distance-based Data Selection
Julien Pallage
Antoine Lesage-Landry
We propose a new unsupervised anomaly detection method based on the sliced-Wasserstein distance for training data selection in machine learn… (see more)ing approaches. Our filtering technique is interesting for decision-making pipelines deploying machine learning models in critical sectors, e.g., power systems, as it offers a conservative data selection and an optimal transport interpretation. To ensure the scalability of our method, we provide two efficient approximations. The first approximation processes reduced-cardinality representations of the datasets concurrently. The second makes use of a computationally light Euclidian distance approximation. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We present the filtering patterns of our method on synthetic datasets and numerically benchmark our method for training data selection. Finally, we employ our method as part of a first forecasting benchmark for our open-source dataset.
2025-04-16
ArXiv (preprint)
doi.org
arxiv.org
Contemporary review of critical illness following allogeneic hematopoietic stem cell transplant in adults.
Laveena Munshi
Guillaume Dumas
Bruno Ferreryro
Cristina Gutierrez
Boris Böll
Pedro Castro
Sanjay Chawla
Matteo Di Nardo
Antoine Lafarge
Colleen McEvoy
Djamel Mokart
Antonio Paulo Nassar
Judith Nelson
Frédéric Pène
Peter Schellongowski
Elie Azoulay
2025-04-15
Intensive Care Medicine (published)
doi.org
Alcohol related hepatitis in intensive care units: clinical and biological spectrum and mortality risk factors: a multicenter retrospective study
Maxime Gasperment
Léa Duhaut
Nicolas Terzi
Côme Gerard
Luc Haudebourg
Alexandre Demoule
Mialy Randrianarisoa
Vincent Castelain
Sacha Sarfati
Fabienne Tamion
Charlene Le Moal
Christophe Guitton
Gabriel Preda
Arnaud Galbois
Thibault Vieille
Gaël Piton
Marika Rudler
Guillaume Dumas
Hafid AIT-OUFELLA
Background Alcohol related hepatitis is responsible for high morbidity and mortality, but little is known about the management of patients w… (see more)ith hepatitis specifically in intensive care units (ICU). Methods Retrospective study including patients with alcohol related hepatitis hospitalized in 9 French ICUs (2006–2017). Alcohol related hepatitis was defined histologically or by an association of clinical and biological criteria according to current guidelines. Results 187 patients (median age: 53 [43–60]; male: 69%) were included. A liver biopsy was performed in 51% of cases. Patients were admitted for impaired consciousness (71%), sepsis (64%), shock (44%), respiratory failure (37%). At admission, median SOFA and MELD scores were 10 [7–13] and 31 [26–40] respectively. 63% of patients received invasive mechanical ventilation, 62% vasopressors, and 36% renal replacement therapy. 66% of patients received corticosteroids, and liver transplantation was performed in 16 patients (8.5%). ICU and in-hospital mortality were 37% and 53% respectively. By multivariate analysis, ICU mortality was associated with SOFA score (without total bilirubin) (SHR 1.08 [1.02–1.14] per one-point increase), arterial lactate (SHR 1.08 [1.03–1.13] per 1 mmol/L) and MELD score (SHR 1.09 [1.04–1.14] per 1 point), while employment was associated with increased survival (HR 0.49 [0.28–0.86]). After propensity score weighting, the use of corticosteroids did not affect ICU mortality in the overall population but had a beneficial effect in the subgroup of patients with histological proof. Patient prognosis was also better in responders assessed by Lille score at day 7 (OR 6.67 [2.44–20.15], p  0.001). Conclusion Alcohol related hepatitis is a severe condition leading to high mortality in ICU patients. Severity of organ failure
2025-04-14
Annals of Intensive Care (published)
doi.org
Assessing Critical Thinking Skills in Data Literacy: A Digital Performance Assessment
Ying Cui
Xiaoxiao Liu
Fu Chen
Alina Lutsyk
Jaqueline P. Leighton
Maria Cutumisu
2025-04-14
Zeitschrift für Pädagogik (published)
doi.org
auto-fpt: Automating Free Probability Theory Calculations for Machine Learning Theory
Arjun Subramonian
Elvis Dopgima Dohmatob
2025-04-13
ArXiv (preprint)
doi.org
arxiv.org
Brain Diffusion Transformer for Personalized Neuroscience and Psychiatry
Rongquan Zhai
Yechen Hu
Liping Zheng
Shitong Xiang
Chao Xie
Lei Peng
Tobias Banaschewski
Gareth J. Barker
Arun L.W. Bokde
Rüdiger Brühl
Sylvane Desrivières
Herta Flor
Hugh Garavan
Penny Gowland
Antoine Grigis
Andreas Heinz
Herve Lemaitre
Jean-Luc Martinot
Marie-Laure Paillère Martinot
Eric Artiges … (see 26 more)
Frauke Nees
Dimitri Papadopoulos Orfanos
Luise Poustka
Michael N. Smolka
Sarah Hohmann
Nathalie Holz
Nilakshi Vaidya
Robert Whelan
Zuo Zhang
Lauren Robinson
Jeanne Winterer
Sinead King
Yuning Zhang
Hedi Kebir
Ulrike Schmidt
Julia Sinclair
Argyris Stringaris
Gunter Schumann
Danilo Bzdok
Henrik Walter
Edmund T. Rolls
Barbara Sahakian
Trevor W. Robbins
Jianfeng Feng
Weikang Gong
Tianye Jia
Task-fMRI analyses typically focus on localized activation contrasts between stimuli, neglecting the brain’s dynamic hierarchy. We introdu… (see more)ce Brain Diffusion Transformer (Brain-DiT), a deep generative model capturing recurrent processing underlying individualized neurocognitive state transitions via functional networks. Without prior assumptions, Brain-DiT identifies canonical cognitive regions in the brain and reveals replicable subgroups with distinct neural circuits in large cohorts, offering critical clinical insights overlooked by traditional methods: individuals exhibiting negative emotion bias, linked to language-related regions, had a 12-fold higher likelihood of major depression, and those with maladaptive inhibition strategies, associated with overactive medial frontal regions, showed a 9-fold increased risk of alcohol abuse. By bridging cognitive theory and psychiatric applications, Brain-DiT provides a unified analytical paradigm, paving the way for operational personalized medicine in psychiatry.
2025-04-13
bioRxiv (preprint)
doi.org
Diverse and feasible retrosynthesis using GFlowNets
Piotr Gainski
Michał Koziarski
Krzysztof Maziarz
Marwin Segler
Jacek Tabor
Marek Śmieja
2025-04-13
Information Sciences (published)
doi.org