# Graph theory in Disorders of Consciousness: Toward multimodal integration

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

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## To cite this version:

Arturo Cabrera Vazquez, Sophie Achard, Michel Dojat, Stein Silva. Graph theory in Disorders of Consciousness: Toward multimodal integration. 2025 - 5th Annual Meeting of the Neuromod Institute, Jul 2025, Antibes, France., pp.1-1, 2025. [https://hal.science/hal-05153617](https://hal.science/hal-05153617)

HAL Id: hal-05153617

https://hal.science/hal-05153617v1

Submitted on 9 Jul 2025

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## Introduction

Disorders of Consciousness (DOC) are prolonged states of altered consciousness, such as coma, categorized based on neurobehavioral function [1]. Graph theory applied to resting-state fMRI has yielded promising insights into how coma affects brain connectivity by analyzing network topology [2]. However, the recovery of consciousness involves mechanisms beyond the hemodynamic signals captured by fMRI [3]. A multimodal, interdisciplinary approach that combines data from multiple imaging techniques offers a more comprehensive understanding of DOC, with direct clinical relevance [4]. However, previous approaches have relied on complex models that difficult its clinical interpretability [5].

## A marker of neuroinflammation

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In a study of 17 comatose patients (6 anoxic, 11 traumatic) and 24 age- and sex-matched healthy controls, Translocator Protein (TSPO) expression was assessed using PET imaging to explore its role in consciousness recovery. Expression patterns varied by etiology and neurological outcome, suggesting that distinct neuroimmune responses may influence recovery trajectories [6].

## Enabling multimodal integration

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Degree $∩$ TSPO: Nodes

1 & 1.4 : [a, b, f]

1 & 1.3 : [g]

2 & 1.2 : [d]

3 & 1.4 : [c, e]

Orthogonality $\mathcal{O}$

$$ \mathcal{O}(\text{Deg, TSPO}) = \frac{7-2}{7} = 0.71 $$

We analyzed the relationship between node metrics from fMRI-based graphs and regional TSPO expression from PET in coma patients scanned 2-30 days post-injury, with minimal inter-scan delay. Graphs were constructed using a 10% threshold on connection strength using the AAL atlas (89 regions). A linear regression tested group-level associations between node metrics and TSPO, computed by averaging per-node values across individuals. Statistical significance was assessed via 10,000 permutation tests (group sizes: 20 controls, 5 anoxic, 9 traumatic; some exclusions due to preprocessing). In parallel, we used nodal equivalent classes [7] in a multimodal framework [8] across graph thresholds to examine alignment between graph structure and TSPO expression.

## References

[1] Eapen et al. (2017). Disorders of Consciousness. *Physical medicine and rehabilitation clinics of North America*, 28(2), 245-258.
[2] Malagurski et al. (2019). Topological disintegration of resting state functional connectomes in coma, *NeuroImage*, 195, 354-361.
[3] Medaglia, John. (2017). Functional Neuroimaging in Traumatic Brain Injury: From Nodes to Networks. *Frontiers in Neurology*, 24:8-407.
[4] Luppi et al. (2021). Mechanisms Underlying Disorders of Consciousness: Bridging Gaps to Move Toward an Integrated Translational Science. *Neurocrit Care* 35 (Suppl 1), 37-54 (2021).

[5] Qi et al. (2024) Classifying disorders of consciousness using a novel dual-level and dual-modal graph learning model. *J Transl Med* 22, 950.

[6] Sarton et al. (2024). Neuroimmune activation is associated with neurological outcome in anoxic and traumatic coma. *Brain*, 147 (4), 1321-1330.

[7] Carboni et al. (2023). Nodal-statistics-based equivalence relation for graph collections. *Phys. Rev. E* 107, 014302

[8] Cabrera Vazquez et al. (2025). Interpretable AI and Graph Theory in Disorders of Consciousness. *IABM 2025 - 3ème Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale*. hal-04998406

## Results

### Linear regression - Degree & TSPO

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### Permutation tests for Degree & TSPO

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Group-level analyses showed a positive node metric-TSPO relationship in controls and negative in coma groups. Degree was the only metric with a significant association (p = 0.033, FDR-corrected). Additional node metrics showed similar trends but were not significant after permutation testing and FDR corrections.

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Group classification using orthogonality showed produced high accuracies (Controls vs. Anoxic: 0.833; Controls vs. Traumatic: 0.821; Anoxic vs. Traumatic: 0.929). K-Means centroids often aligned with outliers, potentially due to limitations of the algorithm and threshold-dependent changes in equivalent class structure. Orthogonality scores declined with increasing graph density as added edges altered topology.

## Perspectives

* Results suggest a group-level link between node metrics and TSPO expression in healthy controls, which appears disrupted in coma.

* In coma, less connected nodes show higher TSPO, indicating reduced functional integration in regions undergoing reparative processes.

* Orthogonality scores decreased with graph density, supporting the multimodal relationship for nodes with unique topological characteristics.

* Our findings remain preliminary due to limited sample size.

* Equivalent classes require refinement to better describe multimodal relationships. Additional measures from the original model [7] should be adapted for this context.

* For the first time, we show a link between neuroinflammation and node metrics in coma using a multimodal framework.