# Using models for classification: real-life applications

Marie-Constance Corsi

► To cite this version:

Marie-Constance Corsi. Using models for classification: real-life applications. BIOMAG 2024 - 23rd International Conference on Biomagnetism, Aug 2024, Sydney, Australia. hal-04701100

HAL Id: hal-04701100

https://inria.hal.science/hal-04701100v1

Submitted on 18 Sep 2024

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Distributed under a Creative Commons Attribution 4.0 International License
Using models for classification:
real-life applications

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Marie-Constance Corsi,
Inria research scientist
NERV team, Paris Brain Institute
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For which purpose(s)?

- Validating potential biomarkers

- Diagnosing diseases

- Distinguishing mental states

...
# Capturing fast, non-linear brain dynamics

Neuronal avalanches: bursts of enhanced activity observed across neuroimaging modalities

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Adapted from [Polverino et al, Neurology, 2022]

## Hypothesis:

The neuronal avalanches could spread differently according to the task/populations & provide original features
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Neuronal avalanche

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Time

Adapted from [Sorrentino et al, eLife, 2021]

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Avalanche Transition Matrix
(ATM)

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Mean transition matrices

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Significant Nodes and Correlation

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Significant edges

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Classification for clinical diagnosis – Temporal Lobe Epilepsy (TLE)

<figure><img src="image_13.png" /><figcaption>Model interpretability</figcaption></figure>

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Brain-Computer Interface (BCI)

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Adapted from (Ahn & Jun, 2015)

**Problem:**

Current BCIs fail to detect the mental intentions in ~30% of users – **BCI inefficiency** (Thompson, 2018)
⇒ Rely on local measurements of the brain activity
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For a complete description of the protocol and the dataset, please refer to [Corsi et al, NeuroImage, 2020]
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# Building innovative BCI protocols based on ATMs

10

## Classification performance

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## Model interpretability

MI vs Rest

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VS

Features importance

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C. Mannino

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# Dealing with (alternative) FC estimators...

* Dealing with high dimensional matrices

* Classifiers [Lotte et al, 2018]

* Dimensionality reduction

* Inter-subject variabilities

* Identifying features associated with robust patterns

* Ensemble learning approach

* Real impact

* Interpretability

* Replicability

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[Corsi et al, IEEE TBME, 2022]

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[Corsi et al, II]

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pyriemann.readthedocs.io/en/latest/

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MOABB

happyfeat.readthedocs.io/en/latest/

moabb.neurotechx.com/docs/
# Acknowledgements

## Paris Brain Institute

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Institut
du Cerveau

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Inserm
Institut national
de la santé et de la recherche médicale

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SORBONNE
UNIVERSITE

Camilla Mannino,
Arthur Desbois,
Tristan Venot,
Juliana Gonzalez-Astudillo,
Mario Chavez,
Denis Schwartz,
Nathalie George,
Laurent Hugueville,
Christophe Gitton
Sophie Dupont,
Fabrizio De Vico Fallani

## Lithuanian University of
Health Sciences

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LITHUANIAN UNIVERSITY
OF HEALTH SCIENCES

Dmytro Klepachevskyi,
Ausra Saudargienė

## Univ. Paris Saclay

université
PARIS-SACLAY

Bruno Aristimunha
Sylvain Chevallier

## Penn University

Ari E. Kahn,
Ankit Khambhati,
Jennifer Stiso,
Arnold Campbell,
Danielle S. Bassett

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UNIVERSITY of PENNSYLVANIA

## Institut de Neuroscience des Systèmes

Pierpaolo Sorrentino

Aix*Marseille
université

## Univ. Paris Dauphine

Florian Yger

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## IRCCS E. Medea

Gian Marco Duma

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LA NOSTRA
FAMIGLIA
Thank you for your attention!

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marie-constance.corsi@inria.com

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MConstanceCorsi

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mccorsi