activity
20242026
most citedDetecting local perturbations of networks in a latent hyperbolic embedding space

10 citations · 17 across the 6 of their papers we have counts for

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Showing q-bio.NCShow all

5 papers · 1 filter

q-bio.NC2026

Graph-based analysis of inflammatory profiles in New Onset Refractory Status Epilepticus (NORSE)

Linon Denis, Martin Guillemaud, Vincent Navarro +2

Background and Objectives: Cryptogenic new-onset refractory status epilepticus (cNORSE) represents one of the most severe forms of status epilepticus, occurring in patients without…

q-bio.NC2025

Low-dimensional representation of brain networks for seizure risk forecasting

Steven Rico-Aparicio, Martin Guillemaud, Alice Longhena +3

Identifying preictal states -- periods during which seizures are more likely to occur -- remains a central challenge in clinical computational neuroscience. In this study, we intro…

q-bio.NC2024★ 2 cited

Geometric representations of brain networks can predict the surgery outcome in temporal lobe epilepsy

Martin Guillemaud, Alice Longhena, Louis Cousyn +4

Epilepsy surgery, particularly for temporal lobe epilepsy (TLE), remains a vital treatment option for patients with drug-resistant seizures. However, accurately predicting surgical…

q-bio.NC2024★ 4 cited

Hyperbolic embedding of brain networks detects regions disrupted by neurodegeneration in Alzheimer's disease

Alice Longhena, Martin Guillemaud, Fabrizio De Vico Fallani +2

Graph theoretical methods have proven valuable for investigating alterations in both anatomical and functional brain connectivity networks during Alzheimer's disease (AD). Recent s…

q-bio.NC2024★ 1 cited

Hyperbolic embedding of brain networks as a tool for epileptic seizures forecasting

Martin Guillemaud, Louis Cousyn, Vincent Navarro +1

The evidence indicates that intracranial EEG connectivity, as estimated from daily resting state recordings from epileptic patients, may be capable of identifying preictal states.…