INRIA Grenoble PhD Seminars

February 2026

Speakers :

• Arturo Cabrera Vazquez, PhD student at STATIFY team (Inria) : Neuroinflammation and disrupted functional connectivity in coma: a graph-theoretical study
  Website Linkedin Slides
  Coma, regardless of etiology, is a major health concern, often leading to lasting cognitive and behavioral impairments. Despite this impact, the structural and functional brain damage underlying coma remains poorly understood.¹ Recent TSPO PET studies have revealed substantial neuroinflammation in cortical and subcortical regions corresponding to key resting-state network nodes.² Resting-state functional connectivity (FC) breakdown in coma has been studied with graph-based measures, such as the Hub Disruption Index (HDI), revealing reorganization of functional networks.³ However, the relationship between neuroinflammation and FC in coma remains unclear.⁴
  To address this gap, we developed new modeling strategies to explore the relationship between neuroinflammation and FC in coma. Single-subject TSPO images were transformed into graphs by comparing within-subject regional neuroinflammatory profiles using the Bhattacharyya coefficient (BC). This process yields networks with nodes aligned across imaging modalities. Network reorganization within each modality was analysed using HDI. Cross-modal relationships were explored by introducing a multilayer notion of node equivalence⁵ that spans modalities and node metrics: nodes are characterized by their roles, defined by node metric values. Their roles are compared across layers of a multiplex network. In this framework, each layer represents relationships between brain regions in a specific modality (FC or TSPO). This approach enables the identification of shared cross-modal patterns, assessed using the Correspondence Structural Pattern Score (CSPS).⁵
  HDI results show that TSPO networks exhibit hub reorganization patterns that parallel those observed in fMRI data, suggesting that neuroinflammation mirrors aspects of functional network disruption in coma. Multilayer, fMRI and TSPO analyses further reveal a shared network structure across modalities that varies by condition, with mismatches between functional and neuroinflammatory hubs. Our findings point to whole-brain, cross-system reorganization in coma, underscoring the importance of unified multimodal frameworks, and suggesting new avenues for identifying mechanistic biomarkers.
1. Dehaene et al. (2021). Experimental and Theoretical Approaches to Conscious Processing. doi.org/10.1016/j.neuron.2011.03.018
2. Sarton et al. (2024). Neuroimmune activation is associated with neurological outcome in anoxic and traumatic coma. doi.org/10.1093/brain/awae045
3. Malagurski et al. (2019). Topological disintegration of resting state functional connectomes in coma. doi.org/10.1016/j.neuroimage.2019.03.012
4. Cabrera Vazquez et al. (2025). Graph theory in Disorders of Consciousness: Toward multimodal integration. https://hal.science/hal-05153617v1
5. Carboni et al. (2023). Nodal-statistics-based equivalence relation for graph collections. doi.org/10.1103/physreve.107.014302



• Joel Ignacio Fierro Ulloa, postdoc at DANCE team (Inria) : Modelling and Control of Microbial Ecosystems
  Google Scholar Slides
  Microbial ecosystems play a central role in aquatic environments, where their dynamics are strongly shaped by physical drivers and resource availability. Among these drivers, light is a key factor controlling phytoplankton growth, both as an energy source and as a limiting resource whose spatial and temporal variability induces complex dynamics. In this talk, we explore how mathematical modelling and control theory can be used to describe, analyze, and influence microbial ecosystem behavior.
  We begin by discussing the influence of light on phytoplankton growth and the main biological and physical mechanisms involved. We then introduce the key modelling hypotheses commonly adopted to derive tractable dynamical models, highlighting the balance between biological realism and mathematical simplicity. These ideas are illustrated through the modelling of biomass dynamics in photobioreactors, which provide a controlled framework for the development and validation of growth and control strategies.
  The seminar then addresses the challenges that arise when extending these models to larger and more complex ecosystems, such as lakes or coastal environments. These challenges include spatial heterogeneity, transport processes, species interactions, and limited observability. Finally, we discuss perspectives and future applications of modelling and control approaches, with a particular focus on ecosystem monitoring, risk mitigation, and the management of harmful algal blooms.