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# A theory-driven approach to data analysis: practical applications

Marie-Constance Corsi

► To cite this version:

Marie-Constance Corsi. A theory-driven approach to data analysis: practical applications. 33rd Annual Computational Neuroscience Meeting, Organization for Computational Neuroscience, Jul 2024, Natal, Brazil. [hal-04701039](https://hal-INRA.hal.science/hal-04701039v1)

HAL Id: hal-04701039

https://inria.hal.science/hal-04701039v1

Submitted on 18 Sep 2024

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Distributed under a Creative Commons Attribution 4.0 International License
A theory-driven approach to data analysis:
practical applications

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Marie-Constance Corsi,
Inria research scientist
NERV team, Paris Brain Institute
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 & more!
What is a Brain-Computer Interface (BCI)?

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

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<figure><img src="image_14.png" /><figcaption>Communication</figcaption></figure>
## Behind the magic...

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## Non-invasive tools

EEG

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NIRS

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MEG

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fMRI

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Behind the magic...

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Adapted from
[Gonzalez-Astudillo, ..., Corsi, & De Vico Fallani, JNE, 2020]
Behind the magic...

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Behind the magic...

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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
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 & provide original markers of BCI performance.
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For a complete description of the protocol and the dataset, please refer to [Corsi et al, NeuroImage, 2020]

## Objective:

Tracking the dynamical features related to motor imagery as compared to rest
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Adapted from [Sorrentino et al, eLife, 2021]

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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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Subject-level

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Group-level

TASK-RELATED
RELIABLE EDGES

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Differences in transition probabilities discriminate mental states

15

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Task-related differences are in edges hinging on pre/motor areas (in most subjects)
Subject-level

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Group-level

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[Corsi*, Sorrentino* et al, iScience, 2024]

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Differences in transition probabilities discriminate mental states

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[Corsi*, Sorrentino* et al, iScience, 2024]

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<figure><img src="image_49.png" /><figcaption>Edge-wise correlation</figcaption></figure>
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NODAL
CORRELATIONS

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REGIONAL
GROUPING

CORRELATED
FUNCTIONAL GROUPS

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Executive

Pre/motor

Parietal

Temporal

Occipital
Differences in transition probabilities relate to BCI scores

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Transition probabilities as alternative features for BCI

MEG

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EEG

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signif
equals
ATM+SVM better
CSP+SVM better

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Reliable functional information of task performance retrieval

* Meaningful information communication among regions on the large-scale & aperiodic and scale-free perturbation

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Power spectra
significant at **group** level

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Neuronal avalanches
significant at **individual** level

⇒ Tracking changes in perturbation spreading while performing different tasks via the avalanches transition matrices
Reliable functional information of task performance retrieval

* Meaningful information communication among regions on the large-scale & aperiodic and scale-free perturbation

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⇒ Focusing on higher-order perturbations to capture functionally-relevant processes & reliable information
Reliable functional information of task performance retrieval

Building innovative BCI protocols based on ATMs

* Outperforms the benchmark in EEG – gold standard in BCI → one step closer to the implementation

* Need to further investigate more suited classification methods

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<figure><img src="image_65.png" /><figcaption>C. Mannino</figcaption></figure>
Reliable functional information of task performance retrieval

- Meaningful information communication among regions on the large-scale & aperiodic and scale-free perturbation

- Building innovative BCI protocols

- Tracking changes in perturbation spreading while performing different tasks

- Focusing on higher-order perturbations to capture functionally-relevant processes & reliable information

Markers of BCI performance

- Current predictors of BCI

- Local measurements – power spectra (Ahn et al, 2015) → replicability issues

- Time-averaged brain interactions (Sugata et al, 2014) & Brain networks metrics [Gonzalez-Astudillo et al, JNE, 2020] → online implementation?

- node strength & learning process [Corsi et al, NeuroImage 2020] ]

- core-periphery properties & multimodal integration [Corsi et al, JNE 2021]

- Spreading of neuronal avalanches

- Patterns behaviorally meaningful (Chialvo et al, 2010)

- Computational fast marker

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Paris Brain Institute

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

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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
UNIVERSITÉ

Penn University

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

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Institut de Neuroscience
des Systèmes

Pierpaolo Sorrentino,

Viktor Jirsa

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Institut de
Neurosciences des
Systèmes
Thank you for your attention!

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marie-constance.corsi@inria.com

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MConstanceCorsi

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mccorsi