Publications

Mitochondria‐nucleus crosstalk characterizes Alzheimer's disease across 1,5 million brain cells
Chloé Savignac
Liam Hodgson
Jack Stanley
Anwesha Bhattacharya
Danilo Bzdok
Emerging insight from stem cell research reinforces Alzheimer's disease (AD) to affect mitochondrial protein expression. Compelling new evid… (voir plus)ence points to mitochondrial reactive oxygen species (ROS) as potential driving player in Aβ toxicity, mediated through glial cells and ultimately impacting neuronal health. A comprehensive understanding of how oxidative phosphorylation variations relate to cell function remains largely unexplored, especially through a cell type lens. Leveraging today's largest single‐nucleus RNA sequencing dataset of AD, we unveil how cell‐type‐specific mitochondrial alterations reverberate in the nuclear transcriptome, in 424 AD patients and healthy controls from ROSMAP. By adopting a supervised latent factor modelling approach, we identified distinct gene modules capturing unique aspects of the mitochondrial crosstalk in 6 major brain cell types across 5,427 nuclear and 13 mitochondrial genes. We found that nuclear‐mitochondrial crosstalk varies distinctly with cell identity, reflecting metabolic demands and functional specialization. In neurons and oligodendrocytes, ATP synthase (complex V) takes a central role, whereas type 1 NADH dehydrogenase (complex I) is more prominent in astrocytes, microglia, and OPCs. Screening across >1 million gene expression profiles from ∼20,000 drug perturbations identified mitochondrial‐nuclear signatures that resemble those activated by parthenolide and niclosamide—two chemical compounds previously associated with oxidative stress and cytotoxicity via ubiquitination—as most predictive of AD. Microglia and OPCs achieved the highest overall classification accuracy, with stronger predictive performance observed in males than in females. Mapping gene module expressions to the Allen Human Brain Atlas revealed shared whole‐brain patterns highlighting the precuneus, which we implicated in ubiquitin‐cascade‐enriched modules. Clinical phenotyping revealed that males with higher AD risk, as indicated by their mitochondrial‐nuclear scores on glial gene modules, exhibited a greater pathological burden, including higher amyloid load, Parkinson's‐like symptoms, and neuroticism‐related traits. Finally, by comparing our findings with 2.5 million CRISPRi‐based perturbations, we identified neural signatures associated with female‐biased transcription factors and fatty acid biosynthesis, while glial signatures were linked to DNA damage and oxidative stress. By integrating multiple layers of biological data from established reference atlases, our analysis of mitochondria‐nuclear crosstalk revealed distinct transcriptional signatures associated with AD risk in glial and neural cells, with these associations exhibiting sex‐biased patterns.
2025-12-22
Alzheimer's & Dementia (publié)
doi.org
Turncoat antibodies unmasked in a model of autoimmune demyelination: from biology to therapy
Reza Taghipour-Mirakmahaleh
Françoise Morin
Yu Zhang
Louis Bourhoven
Louis-Charles Béland
Qun Zhou
Julie Jaworski
Anna Park
Juan Manuel Dominguez
J. Corbeil
Eoin P Flanagan
Romain Marignier
Catherine Larochelle
Steven Kerfoot
Luc Vallières
Autoantibodies contribute to many autoimmune diseases, yet there is no approved therapy to neutralize them selectively. A popular mouse mode… (voir plus)l, experimental autoimmune encephalomyelitis (EAE), could serve to develop such a therapy, provided we can better understand the nature and importance of the autoantibodies involved. Here we report the discovery of autoantibody-secreting extrafollicular plasmablasts in EAE induced with specific myelin oligodendrocyte glycoprotein (MOG) antigens. Single-cell RNA sequencing reveals that these cells produce non-affinity-matured IgG antibodies. These include pathogenic antibodies competing for shared binding space on MOG’s extracellular domain. Interestingly, the synthetic anti-MOG antibody 8-18C5 can prevent the binding of pathogenic antibodies from either EAE mice or people with MOG antibody disease (MOGAD). Moreover, an 8-18C5 variant carrying the NNAS mutation, which inactivates its effector functions, can reduce EAE severity and promote functional recovery. In brief, this study provides not only a comprehensive characterization of the humoral response in EAE models, but also a proof of concept for a novel therapy to antagonize pathogenic anti-MOG antibodies.
2025-12-22
Proceedings of the National Academy of Sciences of the United States of America (publié)
doi.org
Fine-Tuned In-Context Learners for Efficient Adaptation
Jörg Bornschein
Clare Lyle
Yazhe Li
Amal Rannen-Triki
Xu Owen He
Razvan Pascanu
When adapting large language models (LLMs) to a specific downstream task, two primary approaches are commonly employed: (1) prompt engineeri… (voir plus)ng, often with in-context few-shot learning, leveraging the model's inherent generalization abilities, and (2) fine-tuning on task-specific data, directly optimizing the model's parameters. While prompt-based methods excel in few-shot scenarios, their effectiveness often plateaus as more data becomes available. Conversely, fine-tuning scales well with data but may underperform when training examples are scarce. We investigate a unified approach that bridges these two paradigms by incorporating in-context learning directly into the fine-tuning process. Specifically, we fine-tune the model on task-specific data augmented with in-context examples, mimicking the structure of k-shot prompts. This approach, while requiring per-task fine-tuning, combines the sample efficiency of in-context learning with the performance gains of fine-tuning, leading to a method that consistently matches and often significantly exceeds both these baselines. To perform hyperparameter selection in the low-data regime, we propose to use prequential evaluation, which eliminates the need for expensive cross-validation and leverages all available data for training while simultaneously providing a robust validation signal. We conduct an extensive empirical study to determine which adaptation paradigm - fine-tuning, in-context learning, or our proposed unified approach offers the best predictive performance on a concrete data downstream-tasks.
2025-12-21
ArXiv (prépublication)
doi.org
arxiv.org
KerJEPA: Kernel Discrepancies for Euclidean Self-Supervised Learning
Eric Zimmermann
Harley Wiltzer
Justin Szeto
David Alvarez-Melis
Lester Mackey
Recent breakthroughs in self-supervised Joint-Embedding Predictive Architectures (JEPAs) have established that regularizing Euclidean repres… (voir plus)entations toward isotropic Gaussian priors yields provable gains in training stability and downstream generalization. We introduce a new, flexible family of KerJEPAs, self-supervised learning algorithms with kernel-based regularizers. One instance of this family corresponds to the recently-introduced LeJEPA Epps-Pulley regularizer which approximates a sliced maximum mean discrepancy (MMD) with a Gaussian prior and Gaussian kernel. By expanding the class of viable kernels and priors and computing the closed-form high-dimensional limit of sliced MMDs, we develop alternative KerJEPAs with a number of favorable properties including improved training stability and design flexibility.
2025-12-21
arXiv (prépublication)
doi.org
arxiv.org
Latent brain subtypes of chronotype reveal unique behavioral and health profiles across population cohorts
Le Zhou
Karin Saltoun
Justin Marotta
Shambhavi Aggarwal
Jakub Kopal
Julie Carrier
Kai-Florian Storch
Robin I. M. Dunbar
Danilo Bzdok
Chronotype is shaped by the complex interplay of endogenous and exogenous factors. This time-enduring trait ties into societal behaviors an… (voir plus)d is linked to psychiatric and metabolic conditions. Despite its multifaceted nature, prior research has treated chronotype as a monolithic trait across the population, risking overlooking substantial heterogeneity in neural and behavioral fingerprints. To uncover hidden subgroups, we develop a supervised pattern-learning framework integrating three complementary brain-imaging modalities with deep behavioral and health profiling from 27,030 UK Biobank participants. We identify five distinct, biologically valid chronotype subtypes. Each demonstrates unique patterns across brain, behavioral and health profiles. External validation in 10,550 US children from the ABCD Study cohort reveals reversed age distributions and replicates sex-associated brain-behavioral patterns, suggesting that potential divergences between chronotype traits observed throughout adulthood may begin to emerge early in life. These findings highlight underappreciated sources of population variation that echo the rhythm of people’s inner clock.
2025-12-21
Nature Communications (publié)
doi.org
Latent brain subtypes of chronotype reveal unique behavioral and health profiles across population cohorts
Le Zhou
Karin Saltoun
Justin Marotta
Shambhavi Aggarwal
Jakub Kopal
Julie Carrier
Kai-Florian Storch
Robin I. M. Dunbar
Danilo Bzdok
Chronotype is shaped by the complex interplay of endogenous and exogenous factors. This time-enduring trait ties into societal behaviors an… (voir plus)d is linked to psychiatric and metabolic conditions. Despite its multifaceted nature, prior research has treated chronotype as a monolithic trait across the population, risking overlooking substantial heterogeneity in neural and behavioral fingerprints. To uncover hidden subgroups, we develop a supervised pattern-learning framework integrating three complementary brain-imaging modalities with deep behavioral and health profiling from 27,030 UK Biobank participants. We identify five distinct, biologically valid chronotype subtypes. Each demonstrates unique patterns across brain, behavioral and health profiles. External validation in 10,550 US children from the ABCD Study cohort reveals reversed age distributions and replicates sex-associated brain-behavioral patterns, suggesting that potential divergences between chronotype traits observed throughout adulthood may begin to emerge early in life. These findings highlight underappreciated sources of population variation that echo the rhythm of people’s inner clock.
2025-12-21
Nature Communications (publié)
doi.org
Perspective on patient and non-academic partner engagement for the responsible integration of large language models in health chatbots
Nikhil Jaiswal
Yuanchao Ma
Bertrand Lebouché
Dan Poenaru
Marie-Pascale Pomey
Sofiane Achiche
David Lessard
Kim Engler
Zully Montiel
Hector Acevedo
Rodrigo Rosa Gameiro
Leo Anthony Celi
Esli Osmanlliu
Uses of large language models (LLMs) in health chatbots are expanding into high-stakes clinical contexts, heightening the need for tools tha… (voir plus)t are evidence-based, accountable, accurate, and patient-centred. This conceptual, practice-informed Perspective reflects on engaging patients and non-academic partners for the responsible integration of LLMs, grounded in the co-construction of MARVIN (for people living with HIV) and in an emerging collaboration with MIT Critical Data. Organised by the Software Development Life Cycle, we describe: conception/needs assessment with patient partners to identify use cases, acceptable trade-offs, and privacy expectations; development that prioritises grounding via vetted sources, structured human feedback, and data-validation committees including patient partners; testing and evaluation using patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) chosen in collaboration with patients to capture usability, acceptability, trust, and perceived safety, alongside task performance and harmful-output monitoring; and implementation via diverse governance boards, knowledge-mobilisation materials to set expectations, and risk-management pathways for potentially unsafe outputs. Based on our experience with MARVIN, we recommend early and continuous engagement of patients and non-academic partners, fair compensation, shared decision-making power, transparent decision logging, and inclusive, adaptable governance that can evolve with changing models and standards. These lessons highlight how patient partnership can directly shape chatbot design and oversight, helping teams align LLM-enabled tools with patient-centred goals while building accountable, safe, and equitable systems. Health chatbots powered by large language models (LLMs) can make medical information more accessible, but most are developed without meaningful input from the people who will use them. This risks unsafe answers, hidden bias, and tools that mainly work for privileged groups. Our team built a chatbot called MARVIN to support people living with HIV, and we are now adapting it for cancer care and children’s health. Patients, caregivers, and community partners shaped what MARVIN should do, chose which sources it should trust, and tested early versions. Their feedback led to concrete improvements including clearer language, more relevant features, and safeguards against misinformation. We are also partnering with MIT Critical Data, which brings patients, members of the public, clinicians, engineers, and policymakers together at events to find and fix bias in medical AI. We have learned that technical fixes alone are not enough: trust, fairness, and accountability require active involvement of diverse users at every stage. Based on these lessons, we recommend: (1) including patients and non-academic partners from the start so their insights can shape core design decisions; (2) compensating them fairly so participation is sustainable; (3) giving them real decision-making power so their input is not tokenistic; and (4) being transparent about the limits of AI so expectations are realistic. In our experience, responsible health AI depends on the lived expertise of the people it serves.
2025-12-21
Research Involvement and Engagement (publié)
doi.org
Possible Principles for Aligned Structure Learning Agents
Lancelot Da Costa
Tomáš Gavenčiak
David C. Hyland
Mandana Samiei
Cristian Dragos-Manta
Candice Pattisapu
Adeel Razi
Karl Friston
This paper offers a road map for the development of scalable aligned artificial intelligence (AI) from first principle descriptions of natur… (voir plus)al intelligence. In brief, a possible path toward scalable aligned AI rests on enabling artificial agents to learn a good model of the world that includes a good model of our preferences. For this, the main objective is creating agents that learn to represent the world and other agents' world models, a problem that falls under structure learning (also known as causal representation learning or model discovery). We expose the structure learning and alignment problems with this goal in mind, as well as principles to guide us forward, synthesizing various ideas across mathematics, statistics, and cognitive science. We discuss the essential role of core knowledge, information geometry, and model reduction in structure learning and suggest core structural modules to learn a wide range of naturalistic worlds. We then outline a way toward aligned agents through structure learning and theory of mind. As an illustrative example, we mathematically sketch Asimov's laws of robotics, which prescribe agents to act cautiously to minimize the ill-being of other agents. We supplement this example by proposing refined approaches to alignment. These observations may guide the development of artificial intelligence in helping to scale existing, or design new, aligned structure learning systems.
2025-12-21
Neural Computation (publié)
doi.org
arxiv.org
The Historical Literature of Nicolae Filimon and the Reconciliation of Realism with the Tradition of the Popular Novel
A.R. Olteanu
2025-12-21
Philobiblon. Transylvanian Journal of Multidisciplinary Research in the Humanities (publié)
doi.org
Coord2Region: A Python Package for Mapping 3D Brain Coordinates to Atlas Labels, Literature, and AI Summaries
Hamza Abdelhedi
Yorguin-Jose Mantilla-Ramos
Sina Esmaeili
Annalisa Pascarella
Vanessa Hadid
Karim Jerbi CoCo Lab
We present Coord2Region, an open-source Python package that streamlines coordinate-based neuroimaging workflows by automatically mapping 3D … (voir plus)brain coordinates (e.g., MNI or Talairach) to anatomical regions across multiple atlases. The package links mapped coordinates to meta-analytic resources via the Neuroimaging Meta-Analysis Research Environment (NiMARE) , providing direct integration with Neurosynth and NeuroQuery. This directly connects coordinates and regions to the broader neuroimaging literature. In addition to atlas-based labeling and literature retrieval, Coord2Region offers an optional large language model (LLM) functionality that generates text summaries of linked studies and illustrative images of queried regions. These AI-assisted features are intended to support interpretation and exploration, while remaining clearly complementary to peer-reviewed literature and established neuroimaging tools. Coord2Region provides a unified pipeline with a robust command-line interface, flexible dataset management, and provider-agnostic LLM utilities, and it supports both single-coordinate and high-throughput batch queries with nearest-region fallback for volume and surface atlases. Furthermore, Coord2Region includes a web interface for interactive configuration (via JSON Schema forms) and cloud execution (via Hugging Face), enabling users to build YAML configurations and run analyses in-browser without local installation. Together, these capabilities lower friction, reduce manual errors, and improve reproducibility in coordinate-centric neuroimaging workflows, promoting more robust and transparent research practices.
2025-12-19
ArXiv (prépublication)
arxiv.org
arxiv.org
E-RGB-D: Real-Time Event-Based Perception with Structured Light
Seyed Ehsan Marjani Bajestani
Giovanni Beltrame
Event-based cameras (ECs) have emerged as bio-inspired sensors that report pixel brightness changes asynchronously, offering unmatched speed… (voir plus) and efficiency in vision sensing. Despite their high dynamic range, temporal resolution, low power consumption, and computational simplicity, traditional monochrome ECs face limitations in detecting static or slowly moving objects and lack color information essential for certain applications. To address these challenges, we present a novel approach that integrates a Digital Light Processing (DLP) projector, forming Active Structured Light (ASL) for RGB-D sensing. By combining the benefits of ECs and projection-based techniques, our method enables the detection of color and the depth of each pixel separately. Dynamic projection adjustments optimize bandwidth, ensuring selective color data acquisition and yielding colorful point clouds without sacrificing spatial resolution. This integration, facilitated by a commercial TI LightCrafter 4500 projector and a monocular monochrome EC, not only enables frameless RGB-D sensing applications but also achieves remarkable performance milestones. With our approach, we achieved a color detection speed equivalent to 1400 fps and 4 kHz of pixel depth detection, significantly advancing the realm of computer vision across diverse fields from robotics to 3D reconstruction methods. Our code is publicly available: https://github.com/MISTLab/event_based_rgbd_ros
2025-12-19
arXiv (prépublication)
doi.org
arxiv.org
Responsible AI measures dataset for ethics evaluation of AI systems
Shalaleh Rismani
Leah Davis
Bonam Mingole
Negar Rostamzadeh
Renee Shelby
AJung Moon
Meaningful governance of any system requires the system to be assessed and monitored effectively. In the domain of Artificial Intelligence (… (voir plus)AI), global efforts have established a set of ethical principles, including fairness, transparency, and privacy upon which AI governance expectations are being built. The computing research community has proposed numerous means of measuring an AI system's normative qualities along these principles. Current reporting of these measures is principle-specific, limited in scope, or otherwise dispersed across publication platforms, hindering the domain's ability to critique its practices. To address this, we introduce the Responsible AI Measures Dataset, consolidating 12,067 data points across 791 evaluation measures covering 11 ethical principles. It is extracted from a corpus of computing literature (n = 257) published between 2011 and 2023. The dataset includes detailed descriptions of each measure, AI system characteristics, and publication metadata. An accompanying, interactive visualization tool supports usability and interpretation of the dataset. The Responsible AI Measures Dataset enables practitioners to explore existing assessment approaches and critically analyze how the computing domain measures normative concepts.
2025-12-19
Scientific Data (publié)
doi.org