# Neuroinflammation and disrupted functional connectivity in coma: a graph-theoretical study

Arturo Cabrera Vazquez, Sophie Achard, Michel Dojat, Stein Silva

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Arturo Cabrera Vazquez, Sophie Achard, Michel Dojat, Stein Silva. Neuroinflammation and disrupted functional connectivity in coma: a graph-theoretical study. EBRAINS Summit 2025, Dec 2025, Brussels, Belgium. 2025. [hal-05421030](https://hal.science/hal-05421030v1)

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Submitted on 17 Dec 2025

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# Neuroinflammation and disrupted functional connectivity in coma: a graph-theoretical study

UGA
Université
Grenoble Alpes

<figure><img src="image_2.png" /></figure>

CHU
TOULOUSE

Arturo Cabrera Vazquez¹,²,³✉, Sophie Achard¹, Michel Dojat¹,², Stein Silva³

¹ Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France.
² Univ. Grenoble Alpes, INSERM U1216, Grenoble Institut Neurosciences, GIN, Grenoble
³ INSERM U1214, Toulouse Neuroimaging Center, CHU Purpan, 31059 Toulouse, France
✉ arturo.cabrera-vazquez@inria.fr

## Background

Coma causes lasting cognitive and behavioral deficits [1]. Understanding its recovery mechanisms is critical for outcome biomarkers [2,3], requiring a multimodal approach spanning imaging, modeling, and clinical practice [4].

TSPO-PET imaging reveals neuroinflammation in key resting-state network regions of coma patients [5], while graph-based analyses show functional network reorganization in coma [6]. The relationship between neuroinflammation and functional connectivity, however, remains unclear [7].

Motivated by these challenges, we developed new modeling strategies to explore the relationship between neuroinflammation and functional connectivity in coma.

## TSPO graphs

<figure><img src="image_3.png" /></figure>

Regional TSPO binding potential maps are normalized per subject and compared using the Bhattacharyya Coefficient (BC) to quantify similarity between regions. Subject-level BC matrices define TSPO graphs, where edges link regions with similar neuroinflammatory profiles, and nodes matching the brain regions used in the fMRI graphs.

## Multimodal equivalent classes

<figure><img src="image_4.png" /></figure>

We introduce a notion of node equivalence spanning multiple node metrics and layers of a multiplex network.

fMRI Clustering & TSPO Degree & Nodes
0.33 & 2 c
1.0 & 2 a, b
0.0 & 1 e
0.0 & 3 d

After integrating multimodal node data across layers, we assess multiplex structure via CSPS [8] for group-wise subject comparisons.

## Analysis & Results

We examined the relationship between fMRI graph structure and our proposed TSPO graphs. Functional and TSPO-PET data were collected from coma patients (n=14, 5 anoxic, 9 traumatic; 6 females. Scanned 2-30 days post-injury. 90-day CRS binarized as favorable/unfavorable) and healthy controls (n=20, 8 females), with minimal inter-scan delay [5].

fMRI graphs were constructed using a 10% connection strength threshold using the AAL atlas (89 regions) [6]. TSPO graphs were built by converting regional voxel TSPO data into subject-level normalized distributions and computing BC between regions. The same brain regions and threshold were applied across modalities.

Building on our previous multimodal integration work [7], within-modality graph analysis is performed using degree-based HDI. Multimodal graph structure is assessed with the CSPS [8], using node metrics across modalities (degree, clustering) and their combinations. Group differences in HDI and CSPS were evaluated with t- and Z-tests, respectively.

### HDI-Degree: fMRI & TSPO

<figure><img src="image_5.png" /></figure>

Hub Disruption Index (HDI) values using degree, computed per imaging modality. Coma patients: anoxic (▲), traumatic (■). 90-day binarized Coma Recovery Scale (CRS) scores are shown by color: favorable, unfavorable. Between-group t-tests were conducted per modality

HDI-fMRI (p=0.0004). HDI-TSPO (p=5.2x10⁻⁷)

### CSPS: fMRI-Clustering & TSPO-Degree

<figure><img src="image_6.png" /></figure>

Within-group Correspondence Structural Pattern Score (CSPS) compares multimodal graph structure between subjects in the same group. Scores near 1 indicate high inter-layer similarity across subjects, with group differences tested with Z-tests.

CSPS Z-test (fMRI-Clustering & TSPO-Degree), p = 0.0007

## Insights

* New methods were developed to study neuroinflammation-functional connectivity relationships in coma.

* HDI allows to explore reorganization of neuroinflammatory and functional networks after brain injury.

* Multimodal equivalent classes enable cross-modality analysis in a unified framework.

<figure><img src="image_7.png" /></figure>

### Neuroinflammatory patterns parallel functional network disruption

A TSPO hub reflects a neuroinflammatory profile shared across many nodes. HDI shows that TSPO and functional hub patterns are both reorganized in coma, with locations diverging across modalities.

<figure><img src="image_8.png" /></figure>

### fMRI & TSPO share a multi-layer structure that is altered in coma

Multimodal CSPS reveals a shared graph structure across imaging modalities that is disrupted in coma. Nodes in well-connected communities in the functional network do not always align with nodes having many neighbors in the TSPO network.

## References

[1] Cronberg et al. (2020). Brain injury after cardiac arrest: from prognostication of comatose patients to rehabilitation. doi.org/10.1016/s1474-4422(20)30117-4
[2] Dehaene et al. (2021). Experimental and Theoretical Approaches to Conscious Processing. doi.org/10.1016/j.neuron.2011.03.018

[3] Edlow et al. (2021). Recovery from disorders of consciousness. doi.org/10.1038/s41582-020-00428-x
[4] Luppi et al. (2021). Mechanisms underlying disorders of consciousness. doi.org/10.1007/s12028-021-01281-6
[5] Sarton et al. (2024). Neuroimmune activation is associated with neurological outcome in anoxic and traumatic coma. doi.org/10.1093/brain/awae045

[6] Malagurski et al. (2019). Topological disintegration of resting state functional connectomes in coma. doi.org/10.1016/j.neuroimage.2019.03.012

[7] Cabrera Vazquez et al. (2025). Graph theory in Disorders of Consciousness: Toward multimodal integration. doi.science/hal-05153617

[8] Carboni et al. (2023). Nodal-statistics-based equivalence relation for graph collections. doi.org/10.1103/physreve.107.014302

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