# Interpretable AI and Graph Theory in Disorders of Consciousness

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

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Arturo Cabrera Vazquez, Sophie Achard, Michel Dojat, Stein Silva. Interpretable AI and Graph Theory in Disorders of Consciousness. IABM 2025 - 3ème Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale, Mar 2025, Nice, France. pp.1-1, 2025. [hal-04998406](https://hal.science/hal-04998406v1)

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Submitted on 20 Mar 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Interpretable AI and Graph Theory in Disorders of Consciousness

Arturo Cabrera Vazquez<fnref n="1" />,³✉, Sophie Achard<fnref n="1" />, Michel Dojat<fnref n="1" />,², Stein Silva³

Inria

Inserm

UGA
Université
Grenoble Alpes

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

CHU
TOULOUSE

## Introduction

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

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

Figure adapted from [5]

Disorders of Consciousness (DOC) are prolonged states of altered consciousness, classified into coma, minimally conscious state (MCS), or unresponsive wakefulness syndrome (UWS) based on neurobehavioral function [1]. One of the biggest current challenges is distinguishing between different levels in DOC, since they affect the patient's recovery [2].

## From brain signals to diagnosis

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

Resting-state (rs) fMRI data is collected from DOC patients, gathered by brain regions, and functional connectivity (FC) is computed based on the signals from each region.

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

The brain is modeled as a graph, with nodes as regions and edges based on thresholded FC. We analyze graph statistics to study network topology and its link to DOC [3].

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

Machine learning (ML) classifies patients based on graph characteristics [4], but different approaches to manipulating graphs and dimensionality reduction in ML algorithms reduce interpretability.

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

Relying only on resting-state FC limits the study of DOC [5]. A methodology combining data from multiple imaging modalities would provide a comprehensive understanding of DOC, benefiting clinical practice [5, 6, 7].

## Multimodality in graphs

Two or more nodes form an equivalent class if they share a statistic's value. Trivial classes contain a single node.

<figure><img src="image_9.png" /><figcaption>Degree : Nodes</figcaption></figure>

1 : [a, b, f, g]
2 : [d]
3 : [c, e]

Orthogonality quantifies shared nodes in trivial classes by relating total nodes to their intersection across equivalent classes [8].

<figure><img src="image_10.png" /><figcaption>CBF : Nodes</figcaption></figure>

123.0 : [d]
14.0 : [a, b, c, e, f]
13.0 : [g]

Deg ∩ CBF : Nodes

1 & 14.0 : [a, b, f]
1 & 13.0 : [g]
2 & 123.0 : [d]
3 & 14.0 : [c, e]

Orthogonality $\mathcal{O}$

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

## Results

<figure><img src="image_11.png" /><figcaption>$\mathcal{O}(\text{Degree, Closeness})$</figcaption></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /><figcaption>$\mathcal{O}(\text{Degree, CBF})$</figcaption></figure>

<figure><img src="image_14.png" /></figure>

rs-fMRI and cerebral blood flow (CBF) were acquired from rats under the anesthetics Isoflurane (ISO, n=6) and Medetomidine (MED, n=7). Graphs with 51 nodes were constructed following the pipeline in [9] and analyzed with the methods in [8]. Degree and closeness centrality were computed from fMRI graphs, and equivalent classes were defined based on these statistics and CBF values. Orthogonality was calculated on graphs at different FC strength thresholds, referred to as costs (e.g., a cost of 0.1 retains only the strongest 10% of connections). These values were classified using K-Means clustering (K=2). Orthogonality of data coming from different modalities resulted in clusters with less overlap and improved classification accuracy in the studied groups.

## The next steps

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

### Work in progress

We'll apply the methods shown here to DOC populations to provide insights into how these metrics differentiate states of consciousness.

### An intuitive approach

We're refining how to integrate multimodal data in graph studies. Our approach simplifies multimodal data fusion compared to earlier approaches.

<figure><img src="image_17.png" /></figure>

### Boosting AI

Multimodality enhances AI performance by integrating diverse data for more accurate, robust, and interpretable decision-making.

<figure><img src="image_18.png" /></figure>

### An integrative understanding

Neuroscientists, AI experts, and clinicians should collaborate in an interdisciplinary framework to leverage multimodal integration for a comprehensive understanding of the brain.

The brain is multimodal. Your model should be too.

<figure><img src="image_19.png" /></figure>

## References

1. Eapen et al. (2017). Disorders of Consciousness. *Physical medicine and rehabilitation clinics of North America*, 28(2), 245-258.
2. Qi et al. (2024) Classifying disorders of consciousness using a novel dual-level and dual-modal graph learning model. *J Transl Med*, 22, 950.
3. Malagurski et al. (2019). Topological disintegration of resting state functional connectomes in coma, *NeuroImage*, Volume 195, 2019, Pages 354-361, 1053-8119.
4. Yu et al. (2021). A multi-domain prognostic model of disorder of consciousness using resting-state fMRI and laboratory parameters. *Brain imaging and behavior*, 15(4), 1966-1976.

5. Carrière et al. (2020). Auditory localization should be considered as a sign of minimally conscious state based on multimodal findings. *Brain communications*, 2 (2), fcaa195.
6. Medaglia, John. (2017). Functional Neuroimaging in Traumatic Brain Injury: From Nodes to Networks. *Frontiers in Neurology*, 8 (407)
7. 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).
8. Carboni et al. (2023). Nodal-statistics-based equivalence relation for graph collections. *Phys. Rev. E* 107, 014302 (14)
9. Becq et al. (2020). Functional connectivity is preserved but reorganized across several anesthetic regimes. *Neuroimage*, 219, 116945

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<footnote n="1">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</footnote>